面向精确定位的钢包运输车车号罐号识别算法

为了提高基于计算机视觉的钢包运输车车号罐号检测定位的准确性，降低在污损情况下的检测误差，减少罐号面积较小导致的漏检问题以及提升检测速度，提出一种基于改进YOLOv5网络的车号罐号检测识别方法。通过在特征提取网络中加入注意力机制，增强模型的特征提取能力；通过将骨干网络升级为轻量级的GhostBottleNeck加快了模型的推理速度；通过对目标字符进行仿射变换，将扭曲变形字符转换为接近正面视角，进而利用改进的ResNet网络进行单字符识别。结果表明，改进后的网络在钢包车号定位的精度达到了90.3%，召回率为87.3%，最终号码识别准确率为97.7%，说明该方法可有效实现钢包运输车车号罐号的精确定位与识别，为智能化管理提供可靠的数据支持。

An algorithm to identify the vehicle number and tank number of ladle truck for accurate location

In order to improve the accuracy of the detection and positioning of the number of ladle carrier tank based on computer vision, reduce the detection error in the case of contamination, reduce the missing detection problem caused by the small area of the number of tank and improve the detection speed, a detection and recognition method of the number of ladle carrier tank based on improved YOLOv5 network was proposed. The feature extraction capability of the model was enhanced by adding attention mechanism into the feature extraction network. By upgrading the backbone network to lightweight GhostBottleNeck, the reasoning speed of the model is accelerated. By performing Affine Transformation on the target character, the distorted character is converted into a near-positive perspective, and then the improved ResNet network is used for single-character recognition. The results show that the accuracy of the improved network is 90.3%, the recall rate is 87.3%, and the final number identification accuracy is 97.7%, indicating that the method can effectively achieve the accurate location and identification of the number of the ladle carrier tank, and provide reliable data support for intelligent management.