Substation foreign object intrusion detection algorithm based on YOLOv5

To address the issue of detecting foreign objects invading substations, which are difficult to detect and easily obscured, this paper suggests an object recognition method utilizing YOLOv5s. Firstly, the C3 module in the backbone network is improved to enhance its feature extraction capability. Secondly, the DCNv2 convolution and SimAM attention mechanism are used to improve SPPFCSPC and serve as the model's spatial pyramid pooling module, allowing the model to better utilize the feature information between the lower and upper layers, thereby improving model performance. Lastly, F-CIOU is used as the model's loss function to balance the issue of significant differences in the number of easy and difficult samples, further enhancing the model's detection accuracy. The experimental findings indicate that the enhanced model's mean average precision (mAP) has risen by 2.1% over the baseline model, achieving a mAP of 91.9%, which suffices for real-world detection requirements.