YOLO-RFF: An Industrial Defect Detection Method Based on Expanded Field of Feeling and Feature Fusion

Abstract

Aiming at the problems of low efficiency, high false detection rate, and poor real-time performance of current industrial defect detection methods, this paper proposes an industrial defect detection method based on an expanded perceptual field and feature fusion for practical industrial applications. First, to improve the real-time performance of the network, the original network structure is enhanced by using depth-separable convolution to reduce the computation while ensuring the detection accuracy, and the critical information extraction from the feature map is enhanced by using MECA (More Efficient Channel Attention) attention to the detection network. To reduce the loss of small target detail information caused by the pooling operation, the ASPF (Atrous Spatial Pyramid Fast) module is constructed using dilate convolution with different void rates to extract more contextual information. Secondly, a new feature fusion method is proposed to fuse more detailed information by introducing a shallower feature map and using a dense multiscale weighting method to improve detection accuracy. Finally, in the model optimization process, the K-means++ algorithm is used to reconstruct the prediction frame to speed up the model’s convergence and verify the effectiveness of the combination of the Mish activation function and the SIoU loss function. The NEU-DET steel dataset and PCB dataset is used to test the effectiveness of the proposed model, and compared to the original YOLOv5s, our method in terms of mAP metrics by 6.5% and 1.4%, and in F1 by 5.74% and 1.33%, enabling fast detection of industrial surface defects to meet the needs of real industry.

Power Adapter Appearance Defect Detection Based on Task Feature Decoupling YOLOv8n

“…The YOLOv5-TB model is used for zinc-coated steel defect detection, which can accurately and quickly detect spangled defects on the surface of zinc-coated steel. Gang et al [22] used depth-wise separable convolution on the original YOLO network structure and enhanced the network's feature extraction by incorporating MECA (More Efficient Channel Attention). The ASPF (Atrous Fast Spatial Pyramid) module is developed by employing dilated convolutions with different expansion rates to capture a greater amount of contextual information.…”

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

“…These methods introduce shallower feature maps and employ dense multiscale weighting to fuse more detailed information, thereby improving detection accuracy. Optimization techniques such as the K-means++ algorithm for reconstructing prediction frames have also been integrated to accelerate model convergence, along with the combination of the Mish activation function and the SIoU loss function to further refine model performance [16][17][18].…”

Advancements in Electric Vehicle PCB Inspection: Application of Multi-Scale CBAM, Partial Convolution, and NWD Loss in YOLOv5

“…YOLOv5, known for its efficient detection speed and high accuracy, has garnered widespread attention. However, directly applying the original YOLOv5 model may not fully meet the specific requirements of PCB defect detection, such as issues with class imbalance, multi-scale defects, and complex backgrounds [10].…”