Abstract: Steel surface defect detection is an important challenge in the field of defect detection, there is still a serious situation of leakage and misdetection, and its detection accuracy is directly related to product quality, and may even jeopardize life safety. At the same time, the application of this technology to the actual production needs to consider resource saving and cost reduction. To solve these problems, a method based on the lightweight detection model YOLOv8-FD is introduced. Three major strategies are used：（1）A feature extraction module is added to C2f to better understand and utilize the input image information, and a DCN is introduced to enhance the feature extraction capability and improve the performance of the target detection; （2）A DUFPN is proposed to fuse the contextual features more efficiently, which drastically reduces the number of parameters and computation to achieve the lightweighting of the network; （3）W-CIOU is introduced as a bounding box loss function to better measure the similarity between targets, accelerate convergence, and improve target detection accuracy. The experimental results show that the model improves mAP by 5 percentage points, R by 3.3 percentage points, the amount of parameters by 27%, and the amount of computation by 35% compared with the baseline. In addition, the algorithm is confirmed to have good robustness through validation on the APSPC and VOC2007 datasets.

Key words: defect detection, steel plate defects, YOLOv8, lightweighting

摘要： 钢材表面缺陷检测是缺陷检测领域中的一个重要挑战，当前仍存在漏检误检严重情况，其检测准确度直接关系到产品质量，甚至可能危及生命安全。同时，将这项技术应用到实际生产中需要考虑资源节约和成本降低。为了解决这些问题，引入了一种基于轻量化检测模型YOLOv8-FD的方法。该算法采用了三大策略：（1）在C2f中增加特征提取模块，以更好地理解和利用输入图像信息，并引入DCN以增强特征提取能力，提升目标检测性能；（2）提出了DUFPN来更有效地融合上下文特征，大幅减少参数量和计算量，实现网络的轻量化；（3）引入W-CIOU（Weight-CIOU）作为边界框损失函数，更好地衡量目标之间的相似性，加速收敛，提高目标检测精度。实验结果显示，与基线相比，该模型的mAP提高了5个百分点，R提高了3.3个百分点，参数量减少了27%，计算量减少了35%。此外，通过在APSPC和VOC2007数据集上的验证，证实该算法具有良好的鲁棒性。

关键词: 缺陷检测, 钢板缺陷, YOLOv8, 轻量化