| 卢进南,刘 扬,王连捷,黎 洛.基于改进 YOLOX 的电铲铲齿断裂检测方法[J].电子测量与仪器学报,2023,37(5):46-57 |
| 基于改进 YOLOX 的电铲铲齿断裂检测方法 |
| Electric shovel tooth break detection method based on improved YOLOX |
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| DOI： |
| 中文关键词: 铲齿 目标检测 YOLOX 扩张卷积注意力 CEIOU 模型压缩 |
| 英文关键词:shovel tooth target detection YOLOX dilated convolution attention CEIOU model compression |
| 基金项目:国家自然科学基金(51774162,51874158)项目资助 |
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| 摘要点击次数: 442 |
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| 中文摘要: |
| 电铲是露天采矿中广泛使用的一种大型机械挖掘设备。 在挖掘过程中,铲齿与矿石长时间的直接冲击会造成铲齿过早
的松动甚至断裂,从而导致电铲计划外的停机和生产力的损失。 针对这个问题,提出了一种基于改进 YOLOX 的电铲铲齿断裂
检测方法。 该方法以 YOLOX 为基础,首先针对受光照不均匀等影响导致检测效果差的问题,在特征金字塔网络加入扩张卷积
注意力机制增强目标在复杂背景中的显著度;其次使用 CEIOU(corner efficient intersection over union)损失函数代替原网络损失
函数优化网络的训练过程,进而提高目标的检测精度;最后考虑嵌入式设备本身的计算能力问题,利用模型压缩策略裁剪网络
中冗余通道,减少模型体积并提高检测速度。 在自主构建的 4 200 张 WK-10 型电铲数据集上进行性能测试,实验结果表明:与
YOLOX 网络模型相比,改进后模型的平均检测精度达到了 95. 37%,提高了 1. 95%,检测速度为 46. 1 fps,提升了 8. 4 fps,模型体
积为 31. 74 MB,减少到原来的 32. 9%。 对比多种其他现存方法,所设计的目标检测算法有着精度高、体积小和速度快的优势。 |
| 英文摘要: |
| Electric shovel is a large mechanical excavation equipment widely used in surface mining. During the excavation process, the
prolonged direct impact of the shovel teeth against the ore can cause the shovel teeth to loosen or even break prematurely, resulting in
unplanned downtime and lost productivity of the shovel. To solve this problem, an electric shovel tooth break detection method based on
the improved YOLOX is proposed. This method is based on YOLOX. Firstly, for the problem of poor detection effect caused by uneven
illumination, the dilated convolution attention mechanism is added to the feature pyramid network to enhance the saliency of the target in
the complex background; Secondly, the corner efficient intersection over union(CEIOU) loss function is used to replace the original
network loss function to optimize the network training process, thereby improving the detection accuracy of the target; Finally,
considering the computing power of the embedded device itself, the model compression strategy is used to tailor the redundant channels in
the network to reduce the model volume and improve the detection speed. The performance test is carried out on the self-built 4 200 WK-
10 electric shovel data set. The experimental results show that compared with the YOLOX network model, the average detection accuracy
of the improved model reaches 95. 37%, which is 1. 95% higher, the detection speed is 46. 1 fps, an increase of 8. 4 fps, and the model
size is 31. 74 MB, which is reduced to 32. 9% of the original. Compared with many other existing methods, the designed target detection
algorithm has the advantages of high precision, small size and fast speed. |
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