SMB-YOLOv5: A Lightweight Airport Flying Bird Detection Algorithm Based on Deep Neural Networks

Birds pose a serious threat to the safe operation of aircrafts. Existing object detection methods have achieved good results for big and medium instances; however, for small flying bird instances, drawbacks such as slow speed, low accuracy, and large model size are still present. Therefore, to overcome these shortcomings, we propose the SMB-YOLOv5 model to detect birds near airports. First, we introduce a self-supervised predictive convolution attention block to enable YOLOv5s6 to focus on critical information, thereby enhancing detection performance. Second, we introduce a multi-branch block (MBB) that enhances the expressive capability of the network by incorporating branches with different receptive fields. Third, to enhance the feature fusion capability of the model and detection mAP@50 for small-bird instances, drawing inspiration from the bidirectional feature pyramid network, we reutilize the shallow-level features of the feature extraction network. We also remove some modules to ensure an increased accuracy without excessively inflating the model size. Finally, to increase the convergence speed of the network, we modify its loss function by replacing complete IoU (CIoU) with efficient IoU (EIoU), which improves the detection mAP@50 of the network. Compared to the YOLOv5s6 model, the proposed SMB-YOLOv5 model achieves a 2.6% increase in mAP@50 on the test dataset. The detection speed has reached 24 fps. We find that the SMB-YOLOv5 has a higher mAP@50 than the other algorithms in the test dataset and the lowest number of parameters, and it can be applied in airport bird detection systems to provide more precise bird orientation information for airport bird detection tasks.