Automotive Parts Defect Detection Based on YOLOv7

Various complex defects can occur on the surfaces of small automobile parts during manufacturing. Compared with other datasets, the auto parts defect dataset used in this paper has low detection accuracy due to various defects with large size differences, and traditional target detection algorithms have been proven to be ineffective, which often leads to missing detection or wrong identification. To address these issues, this paper introduces a defect detection algorithm based on YOLOv7. To enhance the detection of small objects and streamline the model, we incorporate the ECA attention mechanism into the network structure’s backbone. Considering the small sizes of defect targets on automotive parts and the complexity of their backgrounds, we redesign the neck portion of the model. This redesign includes the integration of the BiFPN feature fusion module to enhance feature fusion, with the aim of minimizing missed detections and false alarms. Additionally, we employ the Alpha-IoU loss function in the prediction phase to enhance the model’s accuracy, which is crucial for reducing false detection. The IoU loss function also boosts the model’s efficiency at converging. The evaluation of this model utilized the Northeastern University steel dataset and a proprietary dataset and demonstrated that the average accuracy (mAP) of the MBEA-YOLOv7 detection network was 76.2% and 94.1%, respectively. These figures represent improvements of 5.7% and 4.7% over the original YOLOv7 network. Moreover, the detection speed for individual images ranges between 1–2 ms. This enhancement in detection accuracy for small targets does not compromise detection speed, fulfilling the requirements for real-time, dynamic inspection of defects.