Improved YOLOV4-CSP Algorithm for Detection of Bamboo Surface Sliver Defects With Extreme Aspect Ratio

Bamboo surface defect detection provides quality assurance for bamboo product manufacture in industrial scenarios, an integral part of the overall manufacturing process. Currently, bamboo defect inspection predominantly relies on manual operation, but manual inspection is very time-consuming as well as labor-intensive, and the quality of inspection is not guaranteed. A few visual inspection systems based on traditional image processing have been deployed in some factories in recent years. However, traditional machine vision algorithms extract features in tedious steps and have poor performance along with poor adaptability in the face of complex defects. Accordingly, many scholars are committed to seeking deep learning methods to accomplish surface defect detection. However, existing deep learning object detectors struggle with specific industrial defects when directly applied to industrial defect detection, such as sliver defects, especially for ones with extreme aspect ratios. To this end, this paper proposes an improved algorithm based on the advanced object detector YOLOV4-CSP, which introduces asymmetric convolution and attention mechanism. The introduction of asymmetric convolution enhances the feature extraction in the horizontal direction of the bamboo strip surface, improving the performance in detecting sliver defects. In addition, convolutional block attention module(CBAM), a hybrid attention module, which combines channel attention with spatial attention, is utilized to promote the representation ability of the model by increasing the weights of crucial channels and regions. The proposed model achieves outstanding performance in the general categories and excels in the hard-to-detect categories. Some enterprise’s bamboo strip dataset experiments verify that the model can reach 96.74% mAP for the typical six surface defects. Meanwhile, we also observe significant improvements when extending our model to aluminum datasets with similar characteristics.