Bird detection Algorithm Incorporating Attention Mechanism

Abstract

Abstract The safety of the substation is related to the stability of social order and people's daily lives, and the habitat and reproduction of birds can cause serious safety accidents in the power system. In this paper, to solve the problem of low accuracy rate when the YOLOv5l model is applied to the bird-repelling robot in the substation for detection, a C3ECA-YOLOv5l algorithm is proposed to accurately detect the four common bird species near the substation in real time: pigeon, magpie, sparrow and swallow. Four attention modules—Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), an efficient channel attention module (ECA), and Coordinate Attention (CA)—were added to the backbone network at different times—after the C3-3 network layer, before the SPPF network layer, and in the C3 network layer (C3-3, C3-6, C3-9, and C3-3)—to determine the best network detection performance option. After comparing the network mean average precision rates (mAP@0.5), we incorporated the ECA attention module into the C3 network layer (C3-3, C3-6, C3-9, and C3-3) as the final test method. In the validation set, the mAP@0.5 of the C3ECA-YOLOv5l network was 94.7%, which, after incorporating the SE, CBAM, ECA, and CA attention modules before the SPPF network layer following the C3-3 network layer of the backbone, resulted in mean average precisions of 92.9%, 92.0%, 91.8%, and 93.1%, respectively, indicating a decrease of 1.8%, 2.7%, 2.9%, and 1.6%, respectively. Incorporating the SE, CBAM, and CA attention modules into the C3 network layer (C3-3, C3-6, C3-9, and C3-3) resulted in mean average precision rates of 93.5%, 94.1%, and 93.4%, respectively, which were 1.2%, 0.6%, and 1.3% lower than that obtained for the C3ECA-YOLOv5l model.