Vehicle Paint Defect Detection Based on Improved YOLOv8

To address the issues of low accuracy in vehicle paint defect detection, excessive parameters in detection algorithms, and the uneven distribution of easy and hard samples, a vehicle paint detection method based on an improved YOLOv8 is proposed. To enhance scratch defect detection capabilities and reduce model size, a Deformable Attention Transformer (DAT) mechanism is introduced into the backbone network, and Ghost Convolution (GhostConv) replaces the standard Convolution (Conv) modules. Subsequently, to improve feature extraction capabilities and further reduce model size, a C2f Based on Efficient Multiscale Attention (EMA) (C2f-E) module is proposed by combining the FasterBlock module and the EMA attention mechanism. Moreover, to enhance the detection performance for small objects, a network based on the Bidirectional Feature Pyramid Network (BiFPN) is designed. Additionally, by adding a small-object detection head and a multiscale feature fusion branch, a neck pyramid structure named BiFPN with Small Object Detection Head (BiFPN-D) is proposed. Finally, to address the balance issue between difficult and easy samples and improve the detection performance for small object defects, Wise-Intersection over Union version 3 (WIoUv3) is employed as the loss function for training the network. The improved network is trained on a self-built dataset of vehicle paint defect images and subjected to comparative experiments. The results show that, the improved model achieves an increase of 5.5 percentage points in terms of mean Average Precision (mAP@0.5) and a reduction of 1.4×106 in terms of parameter count, compared to YOLOv8n.