YOLO-CF: An Object Detection Model that Improves Feature Expression at Both Coarse-Grained and Fine-Grained Levels for Industrial Surface Image Defect Detection

In industrial surface defect detection, enhancing the model’s capability to express features at both coarse-grained and fine-grained levels is crucial. Accordingly, this paper proposes YOLO Coarse and Fine (YOLO-CF), a novel object detection model that significantly enhances feature expression by integrating innovative feature fusion strategies with an improved network architecture. YOLO-CF incorporates Res2Net and Res2Net block modules, notably improving the expression of fine-grained features without increasing computational complexity. Additionally, this model introduces a multi-scale feature fusion module, which seamlessly integrates coarse and fine-grained information by combining top-down and bottom-up pathways. This enhancement effectively expands the perceptual range and significantly improves the model’s generalization capability, making YOLO-CF a powerful tool for detecting diverse defects in complex industrial images. A hybrid downsampling module is introduced, combining max pooling, average pooling, and convolution operations with a stride of 2 to provide richer feature representations. In the GC10-DET dataset, YOLO-CF achieved a mean average precision ( mAP ) of 59.09%, surpassing the second-ranked RetinaNet by 3.41 percentage points. On the PCB, crack, NEU-DET and Dish-20 public datasets, YOLO-CF achieved mAPs of 97.38%, 84.72%, 73.35%, and 99.54%, at IoU =0.5, respectively. The experimental results indicate that by integrating feature extraction at both coarse-grained and fine-grained levels, YOLO-CF effectively enhances the model’s ability to detect objects of various sizes in complex scenes, demonstrating significant performance improvements. The code is available at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.