In the industrial production line scenario， target detection technology with visual information has become a new hotspot for intelligent production， providing decision-making information for fault detection and elimination. In response to issues such as target occlusion and dense arrangement of small targets in the electric energy meter production line inspection scenario， this study proposes a lightweight target detection algorithm based on YOLOv8n. By introducing the O-GELAN module， the algorithm achieves richer feature levels while maintaining low computational complexity. The neck architecture of feature collection， fusion， and distribution， along with the channel position attention mechanism， enables cross-layer feature fusion. Furthermore， a re-parameterized convolutional detection head is employed to enhance detection efficiency. Experiments conducted on field-collected production line data demonstrate that the improved model’s mAP（0.5） and mAP（0.5∶0.95） have reached 0.994 and 0.828， respectively， with a detection speed of up to 111.5 frames per second. This meets the high precision and real-time requirements of industrial scenarios and can provide auxiliary decision-making for fault elimination.