Improved YOLOv7 for Multi-Target Detection of Infrared Images of Power Equipment

To address issues such as a large number of targets, similar appearance, and easy to miss detection and misdetection when the background and target are of similar color in substation infrared images, a multi-target detection method is proposed for electric power equipment by improving YOLOv7. First, to better retain the shallow information in the infrared image, cavity convolution and mean pooling were introduced into a spatial pyramid pooling cross-stage partial convolution (SPPCSPC) module, to expand the receptive field while preventing small infrared targets from being submerged in the background. Second, to deal with misdetection and detection omission in multi-target detection, a lightweight simple attention module (SimAM) was introduced into the head network to focus on the region of interest. Finally, a hybrid edge regression loss function suitable for small-target detection, combining the normalized Gaussian Wasserstein distance (NWD) and complete intersection over union (CIOU) losses, was chosen to effectively improve the accuracy of target detection at different scales in infrared images. We conducted comparison experiments with seven other representative detection methods using a self-constructed infrared image dataset of power equipment. The experimental results showed that the improved YOLOv7 network model significantly improves leakage detection and reduced false detection. Its mean average precision (mAP) reached 88.9%, which is a significant improvement compared with those of other representative target detection algorithms for infrared multi-target detection of power equipment.