Abstract: Abstract: With the development of smart grid， the stable operation of substation equipment becomes particularly important. However， due to the large size of existing detection models and the high requirements for the deployment of edge devices， the traditional defect detection methods face challenges in application. In order to solve this problem， this paper proposes a lightweight substation equipment defect detection model based on improved YOLOv8. Firstly， the Backbone and Neck parts of the model are optimized by introducing C2f_Faster block and Slim-Neck structure to reduce redundant calculation and memory access and solve the problem of low detection accuracy caused by feature redundancy. Secondly， the design of the Detect_G module further improves the speed and accuracy of the model detection. Finally， a multi-scale attention mechanism based on cross-space is introduced to enhance the detection ability of equipment defects in small target substations. Experimental results show that the proposed algorithm achieves 92.56% mAP， 5.9 M model parameter count and 345.6 fps detection speed on the substation defect dataset， and its performance is superior to other mainstream algorithms such as SSD， Faster R-CNN， YOLOv4， YOLOv7 and YOLOv8.

Key words: Key words: substation equipment defect detection, YOLOv8, attention mechanism, lightweight model