Detection research of insulating gloves wearing status based on improved YOLOv8s algorithm

Abstract

Abstract The safety hazards may be caused by power grid operators not wearing insulating gloves according to regulations for live electrical working. Additionally, existing methods for detecting the wearing status of insulating gloves suffer from low recognition accuracy, slow detection speed, and large memory occupation by weight files. To address these issues, a Mixup-CA-Small-YOLOv8s (MCS-YOLOv8s) algorithm is proposed for detecting the wearing status of insulating gloves. Firstly, the mixup data augmentation technology using image mixing is introduced, increasing the data’s diversity and improving the model’s generalization ability. Secondly, the coordinate attention (CA) module is added to the original backbone network to strengthen the channel and positional information, suppressing the secondary feature information. Finally, a small target detection structure is designed by removing the last bottom feature detection layer in the original neck network and adding a shallow feature. The ability of small targets’ feature extraction is enhanced without increasing too much computation. The experimental results indicate that the mean average precision of the MCS-YOLOv8s algorithm on the test set is 0.912, the detection speed is 87 FPS, and the model’s weight memory occupies 15.7 MB. It is verified that the model has the advantages of high detection accuracy, fast speed, and small weight memory, which has great significance in ensuring the safe and stable operation of the power grid.

Keywords

• Data augmentation
• CA module
• Small target detection structure
• YOLOv8s
• Insulating gloves
• Wearing status detection