YOLOCrane: An Enhanced YOLOv8-Based Algorithm for Robust Crane Detection in Transmission Line Scenarios

Crane detection under transmission lines, crucial for power system safety monitoring, faces challenges in accuracy and generalization, particularly in environments with structural interference e.g., utility poles and dynamic vegetation occlusion. To address these issues, we propose YOLOCrane, an enhanced YOLOv8-based algorithm. First, a simplified backbone network reduces computational complexity by 33 while maintaining robust feature extraction. Second, by incorporating learnable mask parameters and multi-channel fusion, the channel-wise LBP algorithm adaptively extracts texture features across dimensions, addressing the limitations of traditional LBP in fixed 3×3 windows. Finally, a heterogeneous dual-branch attention fusion module integrates convolutional features with LBP texture patterns, enabling complementary learning of spatial and texture information. Experimental results on the CraneLine dataset demonstrate that YOLOCrane achieves an mAP0.5 of 85.8 , surpassing YOLOv8x by 2.4 , YOLOv11x by 2.1 , and RT-DETRx by 1.7 , while improving inference speed by 9.72 FPS. These advancements underscore YOLOCrane's capability to tackle detection challenges in complex environments, providing a robust solution for real-time safety monitoring of transmission lines.