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Abstract：
Addressing the challenges in detecting surface defects on ceramic disks, such as difficulty in detecting small defects, variations in defect sizes, and inaccurate defect localization, we propose an enhanced YOLOv5s algorithm. Firstly, we improve the anchor frame structure of the YOLOv5s model to enhance its generalization ability, enabling robust defect detection for objects of varying sizes. Secondly, we introduce the ECA attention mechanism to improve the model\'s accuracy in detecting small targets. Under identical experimental conditions, our enhanced YOLOv5s algorithm demonstrates significant improvements, with precision, F1 scores, and mAP values increasing by 3.1 %, 3 %, and 4.5 % respectively. Moreover, the accuracy in detecting crack, damage, slag, and spot defects increases by 0.2 %, 4.7 %, 5.4 %, and 1.9 % respectively. Notably, the detection speed improves from 232 frames/s to 256 frames/s. Comparative analysis with other algorithms reveals superior performance over YOLOv3 and YOLOv4 models, showcasing enhanced capability in identifying small target defects and achieving real-time detection.
摘要：
解决检测陶瓷盘表面缺陷的挑战，例如难以检测小缺陷，缺陷尺寸的变化，和不准确的缺陷定位，我们提出了一种增强的YOLOv5s算法。首先,我们改进了YOLOv5s模型的锚架结构，以增强其泛化能力，对不同尺寸的物体进行鲁棒的缺陷检测。其次,我们引入ECA注意机制来提高模型检测小目标的准确性。在相同的实验条件下,我们增强的YOLOV5S算法显示了显著的改进，精确地，F1得分,mAP值增加3.1%，3%,分别为4.5%和4.5%。此外，检测裂纹的准确性，损坏,炉渣,点缺陷增加0.2%，4.7%，5.4%,和分别为1.9%。值得注意的是,检测速度从232帧/s提高到256帧/s。与其他算法的比较分析揭示了优于YOLOv3和YOLOv4模型的性能，展示了在识别小目标缺陷和实现实时检测方面的增强能力。
