Jul 2026· Digital Signal and Computer Communications· Vol 14294, pp. 1429419 - 1429419-5· 0 citations· 8 references
Engineering
TL;DR
Experimental results show that the detection accuracy of the improved YOLOv11 algorithm is significantly better than that of YOLOv5, YOLOv8 and the baseline YOLOv11 and can meet the actual requirements of UAV inspection and provide reliable technical support for transmission line foreign object detection.
Abstract
Aiming at the problems of variable scales, strong background interference and high miss rate of small objects in transmission line foreign object detection, an improved YOLOv11 algorithm fusing the multi-branch feature extraction network MB-FEN and the CBAM attention mechanism is proposed. The algorithm replaces the original Neck structure with MB-FEN to enhance multi-scale feature extraction. The CBAM module is embedded to focus on key features of foreign objects and suppress background interference. Experimental results show that the detection accuracy of the proposed algorithm is significantly better than that of YOLOv5, YOLOv8 and the baseline YOLOv11. It can meet the actual requirements of UAV inspection and provide reliable technical support for transmission line foreign object detection.
To address the poor detection performance of existing models for coal mine conveyor belts under low illumination, an improved algorithm based on YOLOv8n (You Only Look Once version 8 nano) is proposed to reduce false and missed detections of slender metallic objects, small targets, and objects with background-similar t...
Lin-Xuan Li· 2026 International Conferenc...· 0 citations
A high-precision detection method of small foreign bodies in contact net based on improved YOLOv10 based on a Small Object Focal Loss to alleviate the problem of sample imbalance is proposed.
To solve the problem of detecting foreign objects on mine conveyor belts with different backgrounds and variations in target scales, difficulty recognising elongated targets, and missed targets or false negatives for small foreign objects, this paper proposes YOLOv11-SFF, a conveyor belt foreign object detection algori...
Wen-Xin Zhang, Jian-Ming Zheng, Zhong-Kai Ma· Frontiers in Computing and I...· 0 citations
The HPA (Hybrid Pooling Attention) module in the YOLOv8n Neck stage is introduced, which combines average pooling and max pooling with cross space learning to enhance the transmission and fusion of multi-scale crack features and alleviate information attenuation in feature propagation.
Gui-Zhuo Ai, Lang Ma· Academic Journal of Architec...· 0 citations
Foreign object intrusion on railways represents a major hazard threatening operational safety. The characteristics of long distance and small size lead to high missed detection rates and poor scene adaptability in existing general-purpose object detection models. To address this, this paper proposes a railway foreign o...
Ying-Zhu Lin, Peng Dai, Yu-Chuan Hu et al.· Engineering Research Express· 0 citations
To address the issues of large scale variation, weak features of small objects, and complex background interference in optical remote sensing images, this paper proposes an attention-guided adaptive multi-scale fusion object detection method. In the feature extraction network, a Multi-scale Feature Extracted Module (MF...
