基于改进RetinaNet的白酒瓶盖缺陷检测方法

Defect detection method of liquor bottle caps based on improved RetinaNet

针对瓶装白酒包装质检存在的检测准确度低,小目标重合度高导致误检漏检的情况,提出一种基于RetinaNet的目标检测优化算法,主要使用白酒瓶盖瑕疵数据集进行检测。方法将网络Backbone替换为Swin Transformer,其包含的窗口注意力机制运算有效提升瓶盖瑕疵检测精度同时降低复杂度节省了计算量。在Neck阶段使用神经架构搜索特征金字塔网络(FPN)代替FPN,利用自动架构搜索选出最佳特征融合层,为后续检测提供更高质量的模型,最后采用Soft-NMS降低检测框置信度保留一定真实框,有效的防止瓶盖瑕疵过近或重叠造成漏检。实验证明,改进算法能够精准的识别出各类瓶盖瑕疵,检测精度在白酒瓶盖瑕疵数据集达到了93.53%,相较于原网络提升了8.02%。

Aiming at the situation that the quality inspection of bottled liquor packaging has low detection accuracy and high overlap of small targets,resulting in false detection and missed detection,this paper proposes a target detection optimization algorithm based on RetinaNet,which mainly uses the defect dataset of liquor bottle caps for detection.In this method,the network backbone is replaced by Swin Transformer,which contains window attention mechanism calculation,which effectively improves the accuracy of cap defect detection,reduces complexity,and saves calculation.In the neck stage,neural architecture is used to search for FPN instead of FPN,automatic architecture search is used to select the best feature fusion layer to provide a higher quality model for subsequent detection,and finally Soft-NMS is used to reduce the confidence of the detection frame and retain a certain real frame,which effectively prevents the leakage of cap defects caused by too close or overlapping.Experiments show that the improved algorithm in this paper can accurately identify various bottle cap defects,and the detection accuracy reaches 93.53%in the liquor bottle cap defect dataset,which is 8.02%higher than the original network.