Multi-scale attention and dilation network for small defect detection

Although object detection methods have got surprising performance in simple natural scenes, it is still a challenging task to apply them to complex scenes, especially when the detected objects are small which is common in defect detection tasks of industrial scenarios. Motivated by the higher resolution feature maps, the better detection performance on small objects, we increase the resolution of the feature layers in this paper, and introduce convolutional block attention module (CBAM) into the feature aggregation network to better integrate features at different scales and make better use of the information of small defects. For solving the problem of global context information loss caused by limiting the perceptual field by increasing the resolution of the feature layers, we use dilated convolution blocks to expand the perceptual field. Experimental results show that our method improves the mAP(0.5) on the Industrial Surface Defect Dataset from 88.3% in Yolov5 to 89.2%, mAP(0.75) on the DAGM 2007 increases from 76.56% in Yolov5 to 77.65%, mAP on small object increases by 2.6% on Industrial Surface Defect Dataset and 5.8% on DAGM 2007, which proves the effectiveness of our method for small defect detection.