Abstract:
Abstract: Considering the issues of missed detection， slow detection efficiency， and high detection difficulty in the current traditional manual method for detecting illegal buildings in high-rise structures， this paper proposes a YOLOv5s-based algorithm for illegal building detection. Firstly， the coordinate attention mechanism is incorporated into the original framework's backbone section to enhance detection accuracy. Secondly， the bidirectional feature pyramid network （BiFPN） structure is introduced to improve both feature extraction ability and fusion capability across different layers of the model. The loss function is replaced with SIoU to address the problem of matching angles between predicted and actual boxes. Experimental results demonstrate that compared to other commonly used models on our self-built dataset， precision P and mAP value reach 91.36% and 83.45%， respectively. The improved algorithm enhances performance while maintaining high computational speed， meeting both accuracy and timeliness requirements for UAV aerial detection of illegal buildings.

Key words: Key words: identification of illegal buildings, YOLOv5, coordinate attention, BiFPN, SIoU