WB-YOLO: An efficient wild bat detection method for ecological monitoring in complex environments

The study of bat species and their distribution is vital for understanding the origins and transmission pathways of epidemic diseases. However, the detection of wild bats faces significant challenges due to their complex natural habitats and frequent occlusions caused by social behavior. To address these issues, we propose an object detection method based on the improved You Only Look Once version 7 (YOLOv7) to achieve efficient wild bat detection (WB-YOLO). This method integrates a Vision Transformer encoder module to improve global context integration, adopts deformable convolution, and optimizes the spatial pyramid pooling structure for effective multi-scale feature fusion while reducing computational complexity. Furthermore, a hybrid attention mechanism is introduced to capture both spatial and channel information, enhancing robustness in complex environments. Experimental results in a data set of wild bat images collected in Anhui Province demonstrate that WB-YOLO achieves a precision of 90. 7%, a recall of 89. 0%, and a mean average precision (mAP) of 94. 7%, significantly outperforming other deep learning models in detecting bats in complex scenes and under occlusion. Our approach offers an efficient and accurate solution for the detection of wild bats in real time, with potential applications in ecological research and disease prevention. Code and data related to this work are publicly available at https://github.com/macandzzz/WB-YOLO.