Advancing Utility Pole and Sign Detection Through Deep Learning

Deep learning-based object detection has revolutionized Precision Livestock Farming (PLF), yet a critical barrier remains: high-performance Foundation Models (such as SAM 3) are too computationally intensive for edge deployment, while lightweight models (like YOLO) require prohibitive manual annotation efforts. This work proposes a fully automated knowledge distillation pipeline that leverages the Segment Anything Model 3 (SAM 3) to generate zero-shot pseudo-labels for training efficient YOLOv8 detectors. By treating SAM 3 as an offline auto-annotator, we eliminate the manual labeling bottleneck, producing models capable of real-time inference on resource-constrained hardware. We systematically evaluate this approach on the PigLife dataset, comparing SAM 3-supervised models against human-annotated baselines. Results demonstrate that a SAM 3-trained YOLOv8m achieves a mean Average Precision (mAP) of 79.4% without human intervention, while reducing inference latency by approximately 200$\times$ compared to the teacher model. Furthermore, stratified analysis reveals that in low-occlusion scenarios, the automated pipeline achieves detection rates comparable to human benchmarks ($AP_{50} > 99\%$). These findings indicate that foundation models can serve as effective, zero-annotation-cost supervisors, enabling scalable edge computing solutions for smart agriculture.

The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent years, Unmanned Aerial Vehicles (UAVs) combined with deep learning-based vision systems have emerged as an effective solution for automating this process. However, insulator fault detection remains challenging due to small defect regions, heterogeneous fault patterns, complex backgrounds, and varying imaging conditions. To address these challenges, this paper proposes an optimized YOLO26-MoE, a novel object detection architecture that integrates a sparse Mixture-of-Experts (MoE) module into the high-resolution branch of the YOLO26 detector. The proposed modification enables adaptive feature refinement for subtle and diverse fault patterns while preserving the efficiency of a one-stage detection framework. Hyperparameter optimization, final training, and evaluation were coordinated through a tool-augmented Large Language Model (LLM) agent. The proposed model achieved 0.9900 mAP@0.5 and 0.9515 mAP@0.5:0.95, outperforming the latest YOLO versions. These results demonstrate that the proposed model provides an effective and reliable solution for UAV-based insulator fault detection.

Line segment detection has been studied for decades, yet existing methods are typically designed for different detection paradigms. Generic line segment detectors aim to recover all meaningful line segments in an image, whereas recent deep-learning-based approaches mainly target wireframe line segments that describe salient geometric structures. Because these paradigms follow different detection objectives, methods optimized for one often perform poorly on the other. In this work, we propose Adaptive Dual-Constrained Line Aggregation (ADLA), a line extraction framework designed to operate across different line segment detection paradigms. Starting from an edge strength map, ADLA progressively aggregates pixels into candidate line segments under two complementary geometric constraints: orientation coherence and bounded orthogonal distance to an adaptively estimated line model. During aggregation, the line centroid and orientation are dynamically updated using the accumulated supporting pixels, progressively improving the geometric consistency of the estimated line. Edge strength information is further incorporated into orientation estimation, seed selection, model refinement, and segment validation, reducing the need for extensive parameter tuning. Experiments on three publicly available datasets covering generic, wireframe, and Manhattan line segment detection demonstrate consistently strong performance across substantially different annotation settings. ADLA achieves (F^H) scores of 0.8665 on YorkUrban-LineSegment dataset, 0.8720 on ShanghaiTech dataset, and 0.7297 on YorkUrban dataset. These results demonstrate the effectiveness and flexibility of ADLA across different line segment detection paradigms. The source code for this work is publicly available at https://github.com/ChenguangTelecom/adla .