Optimizing YOLO27, YOLO26, YOLO11, and YOLOv8 for Fine-Grained Small-Object Detection and Segmentation in Complex Orchard Environments

Ranjan Sapkota; Manoj Karkee

Figure 1: Accuracy–computational-complexity comparison of YOLO27, YOLO26, YOLO11, YOLOv10, YOLOv9, YOLOv8, and YOLOv7 detection models on COCO. The vertical axis reports mAP

{}_{50:95}^{val}

, while the horizontal axis represents floating-point operations at approximately 640-pixel input resolution. YOLO27 and YOLO26 are emphasized to illustrate the transition from deployment-oriented CNN/end-to-end detection toward scale-adaptive architectures, including query-based YOLO27m/l detection. FLOPs provide a hardware-independent approximation of arithmetic complexity but do not capture memory movement, operator fusion, attention-kernel efficiency, or accelerator utilization. YOLO27 results are preliminary.

Figure 2: Scale-adaptive dual architecture of YOLO27. Compact YOLO27n/s detectors employ a streamlined CNN-based pathway with strengthened high-resolution representations, FPN/PAN feature fusion, fine- and coarse-scale prediction, foreground-alignment supervision, and either one-to-many NMS-based or one-to-one NMS-free detection. In contrast, YOLO27m/l employ query-based detection with learned object queries and transformer decoding. YOLO27m retains a CNN backbone, whereas YOLO27l incorporates an UltraViT backbone. Dashed connections denote training-specific supervision rather than the principal inference pathway.

Figure 3: Representative examples of complex green fruit orchard scenes used in this study. (a) Full-view image of a commercial apple orchard during the early green fruit (fruitlet) development stage, where immature fruitlets are visually camouflaged within dense green canopy foliage. (b) Enlarged canopy region highlighting a dense cluster of approximately ten green fruitlets (yellow dashed circle), illustrating high object proximity, clustering, and background clutter. (c) Fine-grained anatomical annotation of clustered fruitlets into calyx, fruit main body, and peduncle, each representing a small and low-contrast target. (d) Workers performing manual fruitlet thinning in the field, a labor-intensive and costly but essential tree-fruit production operation increasingly constrained by worker shortages.

Figure 4: Experimental workflow for cross-generation YOLO optimization. Orchard data preparation is followed by training and validation of YOLOv8, YOLO11, and YOLO26 variants under conventional and small-object-focused configurations, with subsequent comparative assessment of detection, segmentation, computational efficiency, and deployment suitability.

Figure 5: Overview of the study site and data collection environment for small-object detection and segmentation; (a) Geographical map of Washington State highlighting the location where the data were collected (Prosser, WA); b) Commercial apple orchard used in this study, illustrating the real-world field conditions during image acquisition.

Figure 6: Detailed architecture of YOLO27n/s. The architecture incorporates a streamlined CNN backbone, widened high-resolution features, FPN/PAN feature fusion, and dual-scale prediction using fine (

P_{f}

) and coarse (

P_{c}

) feature maps. Training combines one-to-many supervision with a foreground-alignment branch, whereas deployment uses one-to-one predictions for efficient NMS-free inference.

Figure 7: Architectural overview of the model families and deployment concept considered in this study: (a) YOLO26, (b) YOLO11, and (c) YOLOv8

Component	Specification
Processor (CPU)	AMD Ryzen Threadripper 7960
CPU cores / threads	24 cores / 48 threads
System memory	256 GiB RAM
Graphics processor	NVIDIA RTX 5000 Ada Generation
Reported GPU memory	32,237 MiB CUDA memory ( $\sim$ 33.8 GB device-reported capacity)
GPU device	CUDA device 0

Table 1: Hardware and software environment used for cross-generation YOLO training and evaluation.

Training Component	Conventional Configuration	Small-Object Configuration	Implementation Detail
YOLO families	YOLOv8 / YOLO11 / YOLO26	YOLOv8 / YOLO11 / YOLO26	Instance-segmentation models
Model scales	n, s, m, l, x	n, s, m, l, x	Five scales per generation
Total architectures	15	15	Three generations $\times$ five scales
Model initialization	Pretrained segmentation weights	Pretrained segmentation weights	pretrained=True
Input image size	$640\times 640$	$960\times 960$	Square model-training input
Maximum training epochs	200	200	Nominal principal training schedule

Table 2: Training and augmentation configurations used for the cross-generation YOLO experiments. The conventional configuration used

640\times 640

-pixel inputs, whereas the small-object-focused configuration used

960\times 960

-pixel inputs with additional augmentation controls. Parameters identified as framework-resolved were obtained from the recorded Ultralytics training configuration.

Figure 8: Qualitative comparison of cross-generation YOLO detection and instance segmentation under conventional and small-object-focused training configurations. The leftmost panel shows the representative original orchard image, followed by prediction and corresponding heatmap visualizations obtained using YOLO26, YOLO11, and YOLOv8 at

640\times 640

and

960\times 960

input resolutions. The examples illustrate differences in the localization and segmentation of calyx, fruitlet, and peduncle structures under challenging green-on-green conditions. Overall, the

960\times 960

configurations recover additional small fruitlet instances in several regions, although model-specific differences remain evident, particularly for the substantially smaller and less represented peduncle class.

Model	Resolution	P	R	Box mAP 50	Box mAP 50:95	Mask mAP 50	Mask mAP 50:95
YOLO26n-seg	640	0.568	0.704	0.661	0.363	0.644	0.335
YOLO26n-seg	960	0.661	0.697	0.682	0.414	0.676	0.384
YOLO26s-seg	640	0.565	0.646	0.627	0.377	0.607	0.341
YOLO26s-seg	960	0.618	0.751	0.687	0.425	0.686	0.397
YOLO26m-seg	640	0.648	0.641	0.654	0.403	0.629	0.361
YOLO26m-seg	960	0.588	0.728	0.665	0.419	0.665	0.395

Table 3: Aggregate detection and instance-segmentation performance of YOLO26 segmentation models under 640

\times

640 and 960

\times

960 input resolutions. Results correspond to the aggregate “All” category across calyx, fruitlet, and peduncle classes. Precision (P) and recall (R) correspond to bounding-box detection, while box and mask mAP are reported at IoU = 0.50 and over IoU = 0.50–0.95. Bold values indicate the highest value obtained for each metric across all evaluated YOLO26 configurations.

Model	$\Delta$ P	$\Delta$ R	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
YOLO26n-seg	+0.093	-0.007	+0.021	+0.051	+0.032	+0.049
YOLO26s-seg	+0.053	+0.105	+0.060	+0.048	+0.079	+0.056
YOLO26m-seg	-0.060	+0.087	+0.011	+0.016	+0.036	+0.034
YOLO26l-seg	-0.037	-0.029	-0.028	+0.006	+0.011	+0.020
YOLO26x-seg	+0.111	-0.134	-0.009	+0.009	-0.017	-0.003

Table 4: Absolute change in aggregate YOLO26 performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels. Changes were calculated as

\Delta=\mathrm{Metric}_{960}-\mathrm{Metric}_{640}

.

Model	Resolution	Layers	Parameters	GFLOPs	Preprocess	Inference	Postprocess
					(ms)	(ms)	(ms)
YOLO26n-seg	640	139	2,689,469	9.0	0.1	1.9	0.3
YOLO26n-seg	960	139	2,689,469	9.0	0.2	1.8	0.6
YOLO26s-seg	640	139	10,366,501	34.1	0.3	1.8	0.4
YOLO26s-seg	960	139	10,366,501	34.1	0.2	3.0	0.8
YOLO26m-seg	640	149	23,509,781	121.2	0.1	3.0	0.4

Table 5: Architectural complexity and validation-time processing speed of YOLO26 segmentation models under 640

\times

640 and 960

\times

960 input resolutions. Lower preprocessing, inference, and postprocessing times indicate greater computational efficiency, whereas higher parameter counts and GFLOPs indicate greater model capacity and computational demand.

Figure 9: Training–validation precision–recall (PR) curves for YOLO26 instance-segmentation models trained using the conventional

640\times 640

-pixel input-resolution configuration: (a,b) YOLO26n-seg, (c,d) YOLO26s-seg, (e,f) YOLO26m-seg, (g,h) YOLO26l-seg, and (i,j) YOLO26x-seg. Panels (a,c,e,g,i) present bounding-box PR curves, whereas panels (b,d,f,h,j) present instance-mask PR curves.

Figure 10: Training–validation precision–recall (PR) curves for YOLO26 instance-segmentation models trained using the small-object-focused

960\times 960

-pixel input-resolution configuration: (a,b) YOLO26n-seg, (c,d) YOLO26s-seg, (e,f) YOLO26m-seg, (g,h) YOLO26l-seg, and (i,j) YOLO26x-seg. Panels (a,c,e,g,i) present bounding-box PR curves, whereas panels (b,d,f,h,j) present instance-mask PR curves.

Model	Resolution	P	R	Box mAP 50	Box mAP 50:95	Mask mAP 50	Mask mAP 50:95
YOLO11n-seg	640	0.546	0.690	0.637	0.357	0.622	0.318
YOLO11n-seg	960	0.595	0.705	0.666	0.396	0.669	0.367
YOLO11s-seg	640	0.553	0.668	0.623	0.364	0.621	0.334
YOLO11s-seg	960	0.668	0.641	0.694	0.426	0.692	0.402
YOLO11m-seg	640	0.612	0.699	0.680	0.404	0.678	0.361
YOLO11m-seg	960	0.651	0.642	0.662	0.414	0.661	0.382

Table 6: Aggregate detection and instance-segmentation performance of YOLO11 segmentation models under 640

\times

640 and 960

\times

960 input resolutions. Results correspond to the aggregate “All” category across calyx, fruitlet, and peduncle classes. Precision (P) and recall (R) correspond to bounding-box detection, while box and mask mAP are reported at IoU = 0.50 and over IoU = 0.50–0.95. Bold values indicate the highest value obtained for each metric across all YOLO11 configurations.

Model	$\Delta$ P	$\Delta$ R	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
YOLO11n-seg	+0.049	+0.015	+0.029	+0.039	+0.047	+0.049
YOLO11s-seg	+0.115	-0.027	+0.071	+0.062	+0.071	+0.068
YOLO11m-seg	+0.039	-0.057	-0.018	+0.010	-0.017	+0.021
YOLO11l-seg	-0.079	+0.077	+0.007	+0.025	+0.011	+0.041
YOLO11x-seg	-0.055	+0.075	+0.020	+0.033	+0.024	+0.048

Table 7: Absolute change in aggregate YOLO11 performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels. Changes were calculated as

\Delta=\mathrm{Metric}_{960}-\mathrm{Metric}_{640}

. Positive values indicate improvement under the higher-resolution configuration.

Model	Resolution	Layers	Parameters	GFLOPs	Preprocess	Inference	Postprocess
					(ms)	(ms)	(ms)
YOLO11n-seg	640	114	2,835,153	9.6	0.8	1.0	2.4
YOLO11n-seg	960	114	2,835,153	9.6	0.5	2.8	1.9
YOLO11s-seg	640	114	10,067,977	32.8	0.8	2.3	3.5
YOLO11s-seg	960	114	10,067,977	32.8	0.3	3.8	1.6
YOLO11m-seg	640	139	22,337,625	112.9	0.8	3.8	2.4

Table 8: Architectural complexity and validation-time processing speed of YOLO11 segmentation models under 640

\times

640 and 960

\times

960 input resolutions. Lower preprocessing, inference, and postprocessing times indicate greater computational efficiency, whereas larger parameter counts and GFLOPs indicate greater architectural capacity and computational demand.

Figure 11: Training–validation precision–recall (PR) curves for YOLO11 instance-segmentation models trained using the conventional

640\times 640

-pixel input-resolution configuration: (a,b) YOLO11n-seg, (c,d) YOLO11s-seg, (e,f) YOLO11m-seg, (g,h) YOLO11l-seg, and (i,j) YOLO11x-seg. Panels (a,c,e,g,i) present bounding-box PR curves, whereas panels (b,d,f,h,j) present instance-mask PR curves.

Figure 12: Training–validation precision–recall (PR) curves for YOLO11 instance-segmentation models trained using the small-object-focused

960\times 960

-pixel input-resolution configuration: (a,b) YOLO11n-seg, (c,d) YOLO11s-seg, (e,f) YOLO11m-seg, (g,h) YOLO11l-seg, and (i,j) YOLO11x-seg. Panels (a,c,e,g,i) present bounding-box PR curves, whereas panels (b,d,f,h,j) present instance-mask PR curves.

Model	Resolution	P	R	Box mAP 50	Box mAP 50:95	Mask mAP 50	Mask mAP 50:95
YOLOv8n-seg	640	0.628	0.730	0.660	0.356	0.649	0.325
YOLOv8n-seg	960	0.619	0.628	0.646	0.390	0.652	0.371
YOLOv8s-seg	640	0.656	0.659	0.666	0.390	0.648	0.356
YOLOv8s-seg	960	0.605	0.774	0.692	0.419	0.690	0.390
YOLOv8m-seg	640	0.628	0.735	0.626	0.376	0.609	0.337
YOLOv8m-seg	960	0.640	0.636	0.660	0.395	0.654	0.371

Table 9: Aggregate detection and instance-segmentation performance of YOLOv8 segmentation models under 640

\times

640 and 960

\times

960 input resolutions. Results correspond to the aggregate “All” category across calyx, fruitlet, and peduncle classes. Precision (P) and recall (R) correspond to bounding-box detection. Bold values indicate the highest value obtained for each metric across all YOLOv8 configurations.

Model	$\Delta$ P	$\Delta$ R	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
YOLOv8n-seg	-0.009	-0.102	-0.014	+0.034	+0.003	+0.046
YOLOv8s-seg	-0.051	+0.115	+0.026	+0.029	+0.042	+0.034
YOLOv8m-seg	+0.012	-0.099	+0.034	+0.019	+0.045	+0.034
YOLOv8l-seg	+0.100	-0.061	+0.033	+0.034	+0.039	+0.050
YOLOv8x-seg	+0.039	+0.059	+0.023	+0.039	+0.032	+0.045

Table 10: Absolute change in aggregate YOLOv8 performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels. Changes were calculated as

\Delta=\mathrm{Metric}_{960}-\mathrm{Metric}_{640}

.

Model	Resolution	Layers	Parameters	GFLOPs	Preprocess	Inference	Postprocess
					(ms)	(ms)	(ms)
YOLOv8n-seg	640	86	3,258,649	11.3	0.4	1.2	2.2
YOLOv8n-seg	960	86	3,258,649	11.3	0.2	1.7	2.3
YOLOv8s-seg	640	86	11,780,761	39.9	0.9	2.8	0.9
YOLOv8s-seg	960	86	11,780,761	39.9	0.4	2.4	0.9
YOLOv8m-seg	640	106	27,224,121	104.3	1.4	5.4	1.2

Table 11: Architectural complexity and validation-time processing speed of YOLOv8 segmentation models under 640

\times

640 and 960

\times

960 input resolutions. Lower processing times represent greater computational efficiency, whereas higher parameter counts and GFLOPs represent greater model capacity and computational demand.

Figure 13: Training–validation precision–recall (PR) curves for YOLOv8 instance-segmentation models trained using the conventional

640\times 640

-pixel input-resolution configuration: (a,b) YOLOv8n-seg, (c,d) YOLOv8s-seg, (e,f) YOLOv8m-seg, (g,h) YOLOv8l-seg, and (i,j) YOLOv8x-seg. Panels (a,c,e,g,i) present bounding-box PR curves, whereas panels (b,d,f,h,j) present instance-mask PR curves.

Figure 14: Training–validation precision–recall (PR) curves for YOLOv8 instance-segmentation models trained using the small-object-focused

960\times 960

-pixel input-resolution configuration: (a,b) YOLOv8n-seg, (c,d) YOLOv8s-seg, (e,f) YOLOv8m-seg, (g,h) YOLOv8l-seg, and (i,j) YOLOv8x-seg. Panels (a,c,e,g,i) present bounding-box PR curves, whereas panels (b,d,f,h,j) present instance-mask PR curves.

Figure 15: Qualitative comparison of YOLO26 instance-segmentation results at

640\times 640

and

960\times 960

training resolutions. Red dotted circles highlight additional calyx and fruitlet instances recovered by the higher-resolution configuration, whereas the peduncle remains undetected at both resolutions. Corresponding heatmaps illustrate the enhanced spatial response and anatomical-feature representation achieved with

960\times 960

training.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLO26n-seg	640	0.643	0.841	0.791	0.479	0.643	0.841	0.795	0.476
YOLO26n-seg	960	0.755	0.742	0.803	0.541	0.754	0.759	0.811	0.519
YOLO26s-seg	640	0.703	0.801	0.806	0.530	0.694	0.802	0.801	0.509
YOLO26s-seg	960	0.715	0.826	0.803	0.554	0.738	0.777	0.808	0.534
YOLO26m-seg	640	0.727	0.796	0.813	0.558	0.730	0.799	0.818	0.525

Table 12: Class-wise detection and instance-segmentation performance for the fruitlet class using YOLO26 models under 640

\times

640 and 960

\times

960 input resolutions. Bold values indicate the highest value for each metric.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLO26n-seg	640	0.625	0.813	0.726	0.360	0.601	0.781	0.698	0.328
YOLO26n-seg	960	0.746	0.771	0.756	0.397	0.717	0.755	0.737	0.371
YOLO26s-seg	640	0.618	0.771	0.736	0.401	0.600	0.755	0.718	0.352
YOLO26s-seg	960	0.664	0.792	0.710	0.397	0.704	0.757	0.707	0.366
YOLO26m-seg	640	0.672	0.742	0.698	0.375	0.666	0.736	0.676	0.337

Table 13: Class-wise detection and instance-segmentation performance for the calyx class using YOLO26 models under 640

\times

640 and 960

\times

960 input resolutions. Bold values indicate the highest value for each metric.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLO26n-seg	640	0.434	0.457	0.468	0.250	0.415	0.438	0.441	0.201
YOLO26n-seg	960	0.483	0.577	0.487	0.303	0.478	0.577	0.480	0.263
YOLO26s-seg	640	0.375	0.365	0.339	0.200	0.333	0.337	0.304	0.163
YOLO26s-seg	960	0.477	0.635	0.549	0.325	0.586	0.558	0.542	0.290
YOLO26m-seg	640	0.545	0.385	0.450	0.278	0.490	0.346	0.394	0.220

Table 14: Class-wise detection and instance-segmentation performance for the peduncle class using YOLO26 models under 640

\times

640 and 960

\times

960 input resolutions. Bold values indicate the highest value for each metric.

Class	$\Delta$ Box P	$\Delta$ Box R	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask P	$\Delta$ Mask R	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
Fruitlet	+0.019	-0.020	-0.005	+0.019	+0.019	-0.024	-0.001	+0.022
Calyx	+0.023	+0.006	-0.001	+0.019	+0.027	+0.008	+0.008	+0.027
Peduncle	+0.055	+0.026	+0.039	+0.040	+0.106	+0.069	+0.077	+0.044

Table 15: Mean absolute change in class-wise YOLO26 performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels across all five model scales.

Figure 16: Qualitative comparison of YOLO11 segmentation at

640\times 640

and

960\times 960

training resolutions. Solid red circles highlight peduncles successfully detected and segmented only by the

960\times 960

configuration, demonstrating improved fine-scale anatomical perception while both configurations effectively identify fruitlet and calyx structures.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLO11n-seg	640	0.677	0.851	0.814	0.512	0.674	0.848	0.810	0.485
YOLO11n-seg	960	0.686	0.841	0.835	0.545	0.685	0.841	0.834	0.523
YOLO11s-seg	640	0.670	0.829	0.794	0.519	0.668	0.826	0.798	0.491
YOLO11s-seg	960	0.753	0.780	0.817	0.562	0.753	0.780	0.820	0.540
YOLO11m-seg	640	0.629	0.890	0.819	0.538	0.667	0.884	0.823	0.496

Table 16: Class-wise detection and instance-segmentation performance for the fruitlet class using YOLO11 models under 640

\times

640 and 960

\times

960 input resolutions.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLO11n-seg	640	0.630	0.815	0.746	0.372	0.617	0.799	0.731	0.315
YOLO11n-seg	960	0.668	0.831	0.759	0.393	0.670	0.836	0.771	0.370
YOLO11s-seg	640	0.640	0.815	0.750	0.382	0.630	0.802	0.736	0.348
YOLO11s-seg	960	0.713	0.720	0.744	0.400	0.716	0.723	0.743	0.369
YOLO11m-seg	640	0.548	0.860	0.743	0.393	0.570	0.843	0.729	0.344

Table 17: Class-wise detection and instance-segmentation performance for the calyx class using YOLO11 models under 640

\times

640 and 960

\times

960 input resolutions.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLO11n-seg	640	0.332	0.404	0.351	0.188	0.332	0.404	0.324	0.152
YOLO11n-seg	960	0.432	0.442	0.404	0.250	0.432	0.442	0.401	0.207
YOLO11s-seg	640	0.349	0.361	0.324	0.190	0.367	0.380	0.328	0.164
YOLO11s-seg	960	0.540	0.423	0.521	0.318	0.540	0.423	0.512	0.297
YOLO11m-seg	640	0.657	0.346	0.479	0.280	0.747	0.284	0.481	0.244

Table 18: Class-wise detection and instance-segmentation performance for the peduncle class using YOLO11 models under 640

\times

640 and 960

\times

960 input resolutions.

Class	$\Delta$ Box P	$\Delta$ Box R	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask P	$\Delta$ Mask R	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
Fruitlet	-0.005	+0.011	+0.014	+0.033	-0.011	+0.015	+0.015	+0.042
Calyx	+0.020	+0.003	+0.003	+0.021	+0.022	+0.014	+0.015	+0.036
Peduncle	+0.028	+0.035	+0.048	+0.048	+0.007	+0.043	+0.052	+0.058

Table 19: Mean absolute change in class-wise YOLO11 performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels across all five model scales.

Figure 17: Qualitative comparison of YOLOv8 instance segmentation at

640\times 640

and

960\times 960

training resolutions. Solid red circles highlight peduncles missed by the

640\times 640

model but successfully detected and segmented at

960\times 960

, demonstrating improved preservation and recognition of fine-scale anatomical structures under higher-resolution training.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLOv8n-seg	640	0.734	0.787	0.775	0.492	0.726	0.777	0.770	0.458
YOLOv8n-seg	960	0.765	0.765	0.823	0.557	0.771	0.771	0.824	0.532
YOLOv8s-seg	640	0.749	0.771	0.792	0.516	0.747	0.765	0.782	0.477
YOLOv8s-seg	960	0.684	0.838	0.804	0.543	0.689	0.845	0.807	0.523
YOLOv8m-seg	640	0.724	0.799	0.759	0.494	0.724	0.802	0.759	0.476

Table 20: Class-wise detection and instance-segmentation performance for the fruitlet class using YOLOv8 models under 640

\times

640 and 960

\times

960 input resolutions.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLOv8n-seg	640	0.691	0.748	0.712	0.328	0.691	0.748	0.691	0.300
YOLOv8n-seg	960	0.691	0.755	0.706	0.362	0.696	0.761	0.714	0.337
YOLOv8s-seg	640	0.722	0.764	0.721	0.358	0.715	0.755	0.703	0.332
YOLOv8s-seg	960	0.677	0.831	0.720	0.376	0.671	0.825	0.708	0.351
YOLOv8m-seg	640	0.687	0.771	0.658	0.357	0.696	0.783	0.661	0.315

Table 21: Class-wise detection and instance-segmentation performance for the calyx class using YOLOv8 models under 640

\times

640 and 960

\times

960 input resolutions.

Model	Resolution	Bounding Box				Mask
		P	R	mAP 50	mAP 50:95	P	R	mAP 50	mAP 50:95
YOLOv8n-seg	640	0.459	0.654	0.493	0.249	0.473	0.673	0.484	0.216
YOLOv8n-seg	960	0.402	0.365	0.409	0.251	0.402	0.365	0.419	0.243
YOLOv8s-seg	640	0.497	0.442	0.485	0.295	0.499	0.442	0.458	0.259
YOLOv8s-seg	960	0.455	0.654	0.551	0.338	0.455	0.654	0.556	0.296
YOLOv8m-seg	640	0.473	0.635	0.460	0.276	0.445	0.600	0.407	0.220

Table 22: Class-wise detection and instance-segmentation performance for the peduncle class using YOLOv8 models under 640

\times

640 and 960

\times

960 input resolutions.

Class	$\Delta$ Box P	$\Delta$ Box R	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask P	$\Delta$ Mask R	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
Fruitlet	+0.011	-0.001	+0.026	+0.035	+0.015	-0.001	+0.029	+0.050
Calyx	+0.023	-0.006	+0.013	+0.023	+0.031	-0.002	+0.018	+0.030
Peduncle	+0.021	-0.044	+0.023	+0.035	+0.031	-0.039	+0.051	+0.045

Table 23: Mean absolute change in class-wise YOLOv8 performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels across all five model scales.

YOLOv8
Model	$\Delta$ P	$\Delta$ R	$\Delta$ F1	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
YOLOv8n-seg	-0.009	-0.102	-0.052	-0.014	+0.034	+0.003	+0.046
YOLOv8s-seg	-0.051	+0.115	+0.022	+0.026	+0.029	+0.042	+0.034
YOLOv8m-seg	+0.012	-0.099	-0.039	+0.034	+0.019	+0.045	+0.034
YOLOv8l-seg	+0.100	-0.061	+0.043	+0.033	+0.034	+0.039	+0.050
YOLOv8x-seg	+0.039	+0.059	+0.048	+0.023	+0.039	+0.032	+0.045

Table 24: Paired change in aggregate detection and instance-segmentation performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels. Changes are calculated as

\Delta M=M_{960}-M_{640}

. F1 was derived from aggregate bounding-box precision and recall. Positive values denote improvement under the 960-pixel configuration.

Scale	$\Delta$ P	$\Delta$ R	$\Delta$ F1	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
Nano (n)	+0.044	-0.031	+0.011	+0.012	+0.041	+0.027	+0.048
Small (s)	+0.039	+0.064	+0.049	+0.052	+0.046	+0.064	+0.053
Medium (m)	-0.003	-0.023	-0.013	+0.009	+0.015	+0.021	+0.030
Large (l)	-0.005	-0.004	-0.002	+0.004	+0.022	+0.020	+0.037
Extra-large (x)	+0.032	+0.000	+0.015	+0.011	+0.027	+0.013	+0.030

Table 25: Mean resolution-induced performance change for each model scale across YOLOv8, YOLO11, and YOLO26. Values represent the mean

\Delta M

for the three architecture generations.

Generation	$\Delta$ P	$\Delta$ R	$\Delta$ F1	$\Delta$ Box mAP 50	$\Delta$ Box mAP 50:95	$\Delta$ Mask mAP 50	$\Delta$ Mask mAP 50:95
YOLOv8	+0.018	-0.018	+0.004	+0.020	+0.031	+0.032	+0.042
YOLO11	+0.014	+0.017	+0.012	+0.022	+0.034	+0.027	+0.045
YOLO26	+0.032	+0.004	+0.020	+0.011	+0.026	+0.028	+0.031

Table 26: Mean resolution-induced performance change for each YOLO generation across the five n/s/m/l/x model scales.

YOLOv8
Model	Train 640 (h)	Train 960 (h)	$\Delta$ Train (h)	Train change (%)	$\Delta$ Infer. (ms)	Infer. change (%)
YOLOv8n-seg	0.462	0.792	+0.330	+71.4	+0.5	+41.7
YOLOv8s-seg	0.369	0.724	+0.355	+96.2	-0.4	-14.3
YOLOv8m-seg	0.406	0.615	+0.209	+51.5	+0.8	+14.8
YOLOv8l-seg	0.332	0.791	+0.459	+138.3	+4.7	+104.4
YOLOv8x-seg	0.500	1.214	+0.714	+142.8	+5.7	+96.6

Table 27: Paired changes in training duration and validation-time inference latency between 640

\times

640 and 960

\times

960 configurations. Percentage changes are relative to the corresponding 640-pixel configuration. Positive values indicate increased computational cost.

Figure 18: Resolution-induced change in aggregate detection and segmentation accuracy across 15 YOLO model configurations. Each heatmap cell represents

\Delta

mAP = mAP 960 –mAP 640 for the corresponding model and performance metric. Positive values indicate improved performance under the 960

\times

960 small-object-focused configuration, whereas negative values indicate reduced performance. The strongest and most consistent gains occur for the small-scale YOLO11s and YOLO26s configurations, while several large and extra-large configurations exhibit diminishing returns.

Figure 19: Resolution-induced change in mask mAP 50:95 across YOLO generations and model scales. Each point represents

\Delta\mathrm{mAP}_{50:95}=\mathrm{mAP}_{50:95}^{960}-\mathrm{mAP}_{50:95}^{640}

. Positive values indicate improved instance-segmentation performance when increasing input resolution from 640

\times

640 to 960

\times

960 pixels, whereas negative values indicate reduced performance. YOLO11s exhibits the largest improvement (

\Delta=+0.068

), while YOLO26x shows a slight reduction (

\Delta=-0.003

), demonstrating that the effect of increased spatial resolution depends jointly on architecture generation and model scale.

Generation	Model	Layers	Parameters (M)	GFLOPs	Model size (MB)
YOLOv8	YOLOv8n-seg	86	3.259	11.3	6.8
	YOLOv8s-seg	86	11.781	39.9	23.9
	YOLOv8m-seg	106	27.224	104.3	54.8
	YOLOv8l-seg	126	45.914	210.1	92.3
	YOLOv8x-seg	126	71.724	328.0	144.0
YOLO11	YOLO11n-seg	114	2.835	9.6	6.0

Table 28: Cross-generation architectural complexity and stored model size of the 15 YOLO instance-segmentation architectures. Parameters and GFLOPs correspond to the fused Ultralytics inference models. Model size corresponds to the optimizer-stripped best-performing checkpoint. The architecture is identical for the corresponding 640- and 960-pixel configurations.

		640 $\times$ 640				960 $\times$ 960
Gen.	Model	Pre	Infer.	Post	Total	Pre	Infer.	Post	Total
YOLO26	n	0.1	1.9	0.3	2.3	0.2	1.8	0.6	2.6
	s	0.3	1.8	0.4	2.5	0.2	3.0	0.8	4.0
	m	0.1	3.0	0.4	3.5	0.2	5.9	0.8	6.9
	l	0.2	3.9	0.3	4.4	0.2	7.7	0.6	8.5
	x	0.1	6.3	0.9	7.3	0.2	12.8	0.7	13.7

Table 29: Per-image validation processing time for YOLOv8, YOLO11, and YOLO26 under 640

\times

640 and 960

\times

960 configurations. Total time is calculated as preprocessing + inference + postprocessing. All values are milliseconds per image.

Generation	Model	640 $\times$ 640			960 $\times$ 960
		Completed	Best	Time (h)	Completed	Best	Time (h)
YOLO26	n	200	–	0.933	200	–	1.073
	s	200	–	1.019	200	–	1.076
	m	200	–	0.998	200	–	1.257
	l	200	–	1.051	200	–	1.409
	x	200	–	1.034	200	–	2.124

Table 30: Training duration and convergence behavior of all configurations. “–” indicates that the full 200-epoch schedule was completed and no early-stopping best epoch was reported in the corresponding summary. YOLO26 completed all 200 epochs in every configuration.

Optimizing YOLO27, YOLO26, YOLO11, and YOLOv8 for Fine-Grained Small-Object Detection and Segmentation in Complex Orchard Environments

Abstract

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