Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring

Dongsheng Li; Tianli Ma; Siling Wang; Beibei Duan; Song Gao

cs.CVDec 29, 2025

Edge-Aware and Content-Adaptive Infrared Gas Leak Detection for Industrial Safety Monitoring

Authors: Dongsheng Li, Tianli Ma, Siling Wang, Beibei Duan, Song Gao

Organizations: School of Mechatronic Engineering, Xi’an Technological University, Xi’an, 710021, Shaanxi, China · School of Electronic Information Engineering, Xi’an Technological University, Xi’an, 710021, Shaanxi, China · Shaanxi Shanhua Coal Chemical Co., Ltd., Weinan, 714104, Shaanxi, China

Abstract

Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging because gas plumes are often faint, small, semi-transparent, and weakly bounded. This study proposes an Edge-Aware and Content-Adaptive Feature Fusion Detector (ECAF-Det) for infrared gas leak detection in weak-plume and cluttered thermal scenes. The main methodological contributions of ECAF-Det comprise three task-oriented components. A local--global feature enhancement block preserves fine boundary cues and long-range plume continuity. A multi-scale edge perception module transforms directional-gradient and Gabor-response cues into hierarchical boundary-sensitive structural priors. A content-adaptive sparse routing path aggregation network dynamically regulates multi-scale feature propagation and limits the contribution of less informative cross-scale responses. Experiments on the IIG dataset show that ECAF-Det improves overall and small-plume detection while maintaining moderate computational complexity. On this dataset, ECAF-Det achieves an average precision (AP) of 29.8%, an AP at an IoU threshold of 0.5 AP50 of 84.3%, and a small-object AP of 25.3%. Compared with the Real-Time Detection Transformer with a ResNet-18 backbone (RT-DETR-R18), these values represent improvements of 3.0, 6.5, and 5.4 percentage points, respectively. The model requires 43.7 giga floating-point operations (GFLOPs) and 14.3 M parameters. On the LangGas dataset, ECAF-Det achieves an AP of 36.3% and an AP50 of 68.5%. The AI contribution lies in edge-aware representation learning and content-adaptive sparse feature routing for weak infrared plume perception. The engineering application is automated infrared gas leak detection for industrial safety monitoring, early warning, and remote inspection.

Figures & tables

Figure 1: Performance and computational-complexity comparison of ECAF-Det and representative detectors on the IIG dataset. Panels (a)–(c) show AP, AP 50 , and AP S , respectively. In each panel, the line represents detection performance, while the bars indicate GFLOPs and parameter counts. Numerical values are annotated for all compared methods to facilitate direct performance–complexity comparison.

Figure 2: Overall architecture of the proposed Edge-Aware and Content-Adaptive Feature Fusion Detector (ECAF-Det) for infrared gas leak detection. The framework consists of a multi-scale edge perception module (MSEPM), a plume-oriented local–global backbone, a content-adaptive sparse routing path aggregation network (CASR-PAN), and a decoder-based detection head. MSEPM provides hierarchical boundary-sensitive structural priors to support plume representation, while CASR-PAN uses an importance estimator to generate content-dependent routing weights for adaptive multi-scale feature aggregation. In AIMM-F and AIMM-S, $\beta$ denotes a fixed positive offset in the variation-dependent modulation term, while $\gamma$ denotes the fixed identity-preservation coefficient.

Figure 3: Structure of the plume-oriented local–global backbone. MSEPM generates hierarchical boundary-sensitive structural priors, which are fused with the corresponding backbone features through scale-specific feature-fusion operations. The local–global plume blocks then combine a local branch for preserving fine-grained plume structures with a frequency-domain global branch for capturing broader contextual continuity.

Figure 4: Architectures of the proposed structural-prior generation and content-adaptive routing components. (a) Multi-scale edge perception module (MSEPM) and gradient–Gabor edge operator (GGEO). GGEO combines four fixed

3\times 3

Sobel-like directional-gradient kernels with a depthwise

3\times 3

Gabor-response branch to generate an initial structural representation

E_{0}

. MSEPM then constructs hierarchical structural priors through progressive

2\times 2

max-pooling and

1\times 1

projection. (b) Importance estimator (IE) in CASR-PAN. The estimator integrates global importance, local importance, and feature-diversity cues to generate content-dependent importance maps for adaptive multi-scale feature routing.

Model	AP (%)	AP 50 (%)	AP 75 (%)	AP S (%)	AP M (%)	AP L (%)	GFLOPs (G)	Params (M)
YOLOv10n ( Wang et al., 2024a )	27.0	70.7	10.6	17.6	34.6	38.4	6.5	2.265
YOLOv10m	25.2	65.8	11.2	14.8	34.2	23.6	58.9	15.313
YOLOv11n ( Khanam and Hussain, 2024 )	24.7	72.3	6.7	16.7	31.1	42.8	6.3	2.582
YOLOv11m	25.8	70.4	10.4	14.7	34.5	31.5	67.6	20.030
YOLOv12n ( Tian et al., 2025 )	25.7	75.2	7.2	19.0	30.9	29.2	6.3	2.555
YOLOv12m	25.2	70.7	9.8	18.2	33.3	12.7	67.1	20.105

Table 1: Detection performance comparison of ECAF-Det and representative detectors on the IIG dataset.

Model	AP (%)	AP 50 (%)	AP 75 (%)	AP S (%)	AP M (%)	AP L (%)	GFLOPs (G)	Params (M)
YOLOv10n ( Wang et al., 2024a )	32.3	61.7	30.2	0.1	11.4	39.8	6.5	2.265
YOLOv10m	28.8	56.9	26.8	0.8	14.1	34.9	58.9	15.313
YOLOv11n ( Khanam and Hussain, 2024 )	33.5	65.1	30.1	1.7	14.7	40.5	6.3	2.582
YOLOv11m	32.6	65.1	28.1	1.6	14.5	39.8	67.6	20.030
YOLOv12n ( Tian et al., 2025 )	34.4	65.7	31.0	1.1	15.3	41.3	6.3	2.555
YOLOv12m	32.8	63.6	28.7	1.7	13.8	39.7	67.1	20.105

Table 2: Detection performance comparison of ECAF-Det and representative detectors on the LangGas dataset.

Model	Precision (%)	Recall (%)	F1-score (%)	Missed detection rate (%)	Model inference latency (ms/image)	End-to-end FPS	AP 50 (%)
RT-DETR-R18	86.97	70.60	77.94	29.40	12.53	75.60	77.8
ECAF-Det	86.31	79.44	82.73	20.56	15.46	61.81	84.3

Table 3: Detection-oriented performance comparison between RT-DETR-R18 and ECAF-Det on the IIG test set. Model-inference latency and end-to-end throughput were measured on an NVIDIA GeForce RTX 5070 Ti GPU with a batch size of 4. End-to-end FPS includes preprocessing, model inference, and postprocessing.

Metric	Value
Matched pairs	1062
Pred./GT area ratio, median (IQR)	0.912 (0.788–1.136)
Pred./GT width ratio, median (IQR)	0.960 (0.891–1.065)
Pred./GT height ratio, median (IQR)	0.960 (0.860–1.089)
Normalized center displacement, median (IQR)	0.080 (0.051–0.116)
Area ratio $>1.25$ (%)	17.23

Table 4: Bounding-box geometry statistics for ECAF-Det on the IIG test set. Statistics are computed for one-to-one matched detections with IoU

\geq 0.5

Figure 5: Qualitative comparison of detection results on the IIG dataset. All examples are original thermal infrared samples from the released IIG dataset. Each row shows the results of a different detector. ECAF-Det detects more weak and small plume regions in several challenging cases, but extremely faint and distant plumes remain difficult to localize accurately.

Condition	Model	AP (%)	AP 50 (%)	AP 75 (%)	AR 100 (%)
Plume visibility
Weak	RT-DETR-R18	24.8	75.4	4.5	51.2
	ECAF-Det	29.5	88.0	4.9	53.8
Moderate	RT-DETR-R18	26.9	79.1	9.3	53.9
	ECAF-Det	32.1	87.2	11.9	56.5
Strong	RT-DETR-R18	28.4	74.5	12.1	49.8

Table 5: Challenge-oriented detection performance on the IIG test set.

Local–Global Plume Block	MSEPM	AP	AP 50	AP S	AP M	AP L	GFLOPs (G)	Params (M)
$\times$	$\times$	26.8	77.8	19.9	32.0	37.1	56.9	19.873
$\checkmark$	$\times$	28.5	80.7	22.2	32.8	37.3	51.9	15.965
$\times$	$\checkmark$	27.9	80.4	24.0	30.6	38.1	59.8	21.119
$\checkmark$	$\checkmark$	29.5	81.0	24.3	33.5	38.9	54.8	17.211

Table 6: Ablation study of the plume-oriented local–global backbone on the IIG dataset. The local–global plume block denotes the proposed block for local detail preservation and frequency-domain contextual aggregation. MSEPM denotes the multi-scale edge perception module using GGEO to generate hierarchical boundary-sensitive structural priors.

Plume-Oriented Local–Global Backbone	CASR-PAN	AP	AP 50	AP S	AP M	AP L	GFLOPs (G)	Params (M)
$\times$	$\times$	26.8	77.8	19.9	32.0	37.1	56.9	19.873
$\checkmark$	$\times$	29.5	81.0	24.3	33.5	38.9	54.8	17.211
$\times$	$\checkmark$	29.4	79.1	23.0	33.6	42.2	45.8	16.939
$\checkmark$	$\checkmark$	29.8	84.3	25.3	32.5	42.6	43.7	14.277

Table 7: Ablation study of the complete ECAF-Det on the IIG dataset.

Configuration	AP	AP 50	AP S	AP M	AP L	GFLOPs	Params
All BasicBlocks baseline	26.8	77.8	19.9	32.0	37.1	56.9	19.873
P2–P5 local–global plume blocks	27.4	77.9	21.1	31.1	35.7	47.1	15.725
P4–P5 local–global plume blocks	28.5	80.7	22.2	32.8	37.3	51.9	15.965
P5 local–global plume block	27.0	78.6	22.4	30.2	42.8	54.5	16.743

Table 8: Stage-wise placement of local–global plume blocks in the ResNet-18 backbone on the IIG dataset.

Figure 6: Effective receptive field (ERF) visualization comparison between (a) the proposed plume-oriented local–global backbone and (b) the original ResNet-18 backbone under the same training and inference settings.

Figure 7: Comparison of high-contribution area ratios between the proposed plume-oriented local–global backbone and the original ResNet-18 backbone under different cumulative contribution thresholds.

Figure 8: Performance comparison of different backbone architectures on the IIG dataset. The marked point denotes the proposed plume-oriented local–global backbone.

Figure 9: Visualization results of the proposed gradient–Gabor edge operator (GGEO). (a) Original infrared image; (b) directional-gradient response extracted by the fixed Sobel-like operators; (c) Gabor-response cue obtained from the depthwise Gabor branch; (d) fused structural response combining directional-gradient and Gabor cues.

Figure 10: Visualization of the hierarchical structural representations involved in the multi-scale edge perception module (MSEPM). (a) Original infrared image; (b) $E_{0}$ : the initial structural representation generated by GGEO before downsampling; (c) $E_{1}$ : the representation after one $2\times 2$ max-pooling operation; (d) $E_{2}$ : the representation after two successive max-pooling operations; and (e) $E_{3}$ : the representation after three successive max-pooling operations. The downsampled representations $E_{1}$ , $E_{2}$ , and $E_{3}$ are subsequently projected through scale-specific $1\times 1$ convolutions to form the hierarchical structural priors used by the corresponding backbone stages.

Gradient Directions	AP	AP 50	AP 75	AP S	AP M	AP L	GFLOPs (G)	Params (M)
$0^{\circ}$	26.7	79.8	6.3	21.4	31.1	33.8	59.7	21.117
$0^{\circ}$ , $90^{\circ}$	28.8	81.1	9.4	22.1	33.1	42.3	59.7	21.118
$0^{\circ}$ , $45^{\circ}$ , $90^{\circ}$ , $135^{\circ}$	27.9	80.4	10.3	24.0	30.6	38.1	59.8	21.119

Table 9: Ablation study of gradient directions in GGEO on the IIG dataset.

Figure 11: Effect of the fusion coefficient

\alpha

in GGEO. Performance comparisons under different fixed values of

\alpha

and learnable

\alpha

are reported in terms of (a) AP, (b) AP 50 , and (c) AP S .

Figure 12: Training evolution of the learnable fusion coefficient

\alpha

in GGEO over 200 epochs. After an initial increasing phase,

\alpha

fluctuates within a narrow range, indicating a stable fusion preference during training.

Operator	AP	AP 50	AP 75	AP S	AP M	AP L	GFLOPs	Params
Sobel ( Heath et al., 1998 )	27.9	79.7	8.1	21.5	32.9	33.6	59.4	21.109
Canny ( Canny, 1986 )	25.8	80.0	7.2	22.1	27.7	31.2	60.1	21.110
Laplacian ( Wang, 2007 )	26.7	79.4	8.2	21.9	30.1	29.7	59.8	21.110
GGEO	27.9	80.4	10.3	24.0	30.6	38.1	59.8	21.119

Table 10: Comparison of edge operators and controlled edge-prior replacements within MSEPM on the IIG dataset.

Model	AP	AP 50	AP 75	AP S	AP M	AP L	GFLOPs (G)	Params (M)
Routing-path ablation
Naive additive fusion	25.9	75.9	8.5	23.6	28.0	38.3	45.8	16.939
Without deep-to-mid fusion	27.5	79.5	7.6	22.5	31.1	36.8	45.8	16.939
Without deep-to-shallow fusion	25.7	76.2	6.8	19.2	30.5	40.6	45.8	16.939
Without shallow-to-mid fusion	25.7	75.8	6.2	19.8	29.7	40.2	45.8	16.939
Without mid-level self-fusion	28.3	80.3	8.3	22.3	31.9	43.5	45.8	16.939

Table 11: Ablation study of CASR-PAN components on the IIG dataset. The first group evaluates the adaptive routing paths, while the second group examines the importance-estimator branches and branch-weighting strategy.

Analysis	$\beta$	$\gamma$	AP	AP 50	AP 75	AP S	AP M	AP L
$\beta$ sensitivity ( $\gamma=1.0$ )
$\beta$ variation	0.00	1.00	27.5	78.4	8.6	21.6	31.1	42.6
$\beta$ variation	0.25	1.00	28.2	77.8	10.3	21.9	32.2	35.8
Default	0.50	1.00	29.4	79.1	12.3	23.0	33.6	42.2
$\beta$ variation	0.75	1.00	25.7	76.5	6.8	21.3	28.5	39.0
$\beta$ variation	1.00	1.00	25.9	76.9	7.4	20.7	29.8	36.3

Table 12: Sensitivity analysis of the fixed modulation coefficients

\beta

and

\gamma

in CASR-PAN on the IIG dataset. One coefficient is varied while the other is fixed at its default value.

Model	AP	AP 50	AP 75	AP S	AP M	AP L	GFLOPs (G)	Params (M)
PANet ( Liu et al., 2018 )	27.3	80.9	8.0	22.1	30.0	39.2	103.8	21.463
BiFPN ( Tan et al., 2020 )	27.0	76.6	8.3	20.8	31.3	38.9	64.3	20.300
NAS-FPN ( Ghiasi et al., 2019 )	25.1	76.4	7.5	19.3	29.9	26.1	93.8	19.756
CASR-PAN	29.4	79.1	12.3	23.0	33.6	42.2	45.8	16.939

Table 13: Comparison of different neck structures on the IIG dataset.

Global-context branch	AP	AP 50	AP 75	AP S	AP M	AP L	GFLOPs (G)	Params (M)
$31\times 31$ large-kernel convolution	25.3	74.5	7.3	17.8	31.2	31.6	55.1	18.095
Non-local module	26.6	77.9	8.6	20.3	31.1	33.9	53.6	17.274
Swin shifted-window attention	26.8	76.9	9.6	22.2	29.6	39.3	72.8	31.743
Proposed DCT-based frequency branch	28.5	80.7	8.8	22.2	32.8	37.3	51.9	15.965

Table 14: Comparison of alternative global-context modeling strategies within the local–global plume block on the IIG dataset.

Abbr./Symbol	Meaning	Abbr./Symbol	Meaning
RT-DETR	Real-Time Detection Transformer	ECAF-Det	Edge-Aware and Content-Adaptive Feature Fusion Detector
GGEO	Gradient–Gabor Edge Operator	MSEPM	Multi-Scale Edge Perception Module
CASR-PAN	Content-Adaptive Sparse Routing Path Aggregation Network	IE	Importance Estimator
AIMM-F	Adaptive Information Modulation Module for Fusion	AIMM-S	Adaptive Information Modulation Module for Self-Enhancement
FPN	Feature Pyramid Network	PANet	Path Aggregation Network
BiFPN	Bi-directional Feature Pyramid Network	NAS-FPN	Neural Architecture Search Feature Pyramid Network

Table 15: List of abbreviations and symbols used in this paper.

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May 1, 2026, cs.CVJ/K move · Enter open · S save

Exploring the Limits of End-to-End Feature-Affinity Propagation for Single-Point Supervised Infrared Small Target Detection

Qiancheng Zhou, Wenhua Zhang

Infrared Small Target Detection Propagation Ablation

Open paper AskPDF arXiv

Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods achieve high precision by recovering mask supervision through explicit, offline pseudo-label construction, such as multi-stage active learning and physics-driven mask generation. In this paper, we study a minimalist alternative: generating point-to-mask supervision online through in-batch, point-anchored feature-affinity propagation. We instantiate this paradigm as GSACP, an end-to-end testbed that directly supervises the detector using hard-margin feature affinity gated by local image priors, entirely eliminating external label-evolution loops. This compact design, however, exposes an optimization bottleneck. Because the affinity target is generated from the same feature representation being optimized, training forms a self-referential loop. We theoretically formalize this as \emph{Self-Referential Propagation Drift}, a representation-supervision entanglement that can sharpen true boundaries or distort the feature space to satisfy its own targets. To systematically isolate these failure modes, we apply a protocolized single-variable ablation procedure spanning local EMA teacher decoupling, hard-background contrastive separation, and adaptive support geometry. On the SIRST3 dataset, GSACP-Final establishes a new ultra-low false-alarm operating regime, achieving a highly competitive

0.6674

mIoU while demonstrating a

38\% relative reduction in false-positive artifacts (

\mathrm{Fa}$) compared with PAL. By systematically deconstructing the end-to-end paradigm, we map its performance boundaries and show that in-batch feature propagation provides a compact alternative for deployment scenarios where false-alarm suppression is paramount.

Abstract

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