| 一种具有多尺度感受视野注意力机制的生活垃圾单阶段目标检测方法 |
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| 引用本文： | 魏铖磊,南新元,李成荣,罗杨宇.一种具有多尺度感受视野注意力机制的生活垃圾单阶段目标检测方法[J].环境工程,2022,40(1):175-183. |
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| 作者姓名： | 魏铖磊 南新元 李成荣 罗杨宇 |
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| 作者单位： | 1. 新疆大学 电气工程学院, 乌鲁木齐 830047; |
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| 摘 要： | 生活垃圾种类繁杂,传统垃圾分选工艺的效率及精确度较低,为提高多尺度、不同材质垃圾的检测精度,同时保证垃圾分类的鲁棒性,基于现有深度卷积神经网络和单阶段目标检测算法YOLOv3,提出具有多尺度感受视野注意力机制的ECA_ERFB_s-YOLOv3算法。首先在算法检测器前引入多尺度感受视野模块,使算法能选择合适的感受视野对不同尺度垃圾物体进行匹配,提高了检测精度;然后,使用ResNet50替换原骨架网络Darknet53,在迁移学习条件下,使用高效注意力机制对ResNet50和多尺度感受视野模块中的特征进行自主增强和抑制,提高了算法的鲁棒性。最后,使用K-means算法对锚框进行回归,并设计了锚框的分配方式。消融实验结果表明:ECA_ERFB_s-YOLOv3精度更高,鲁棒性更好;在检测密集堆放的生活垃圾时,算法能较好地满足任务需要,表现出更好的检测效果。
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| 关 键 词： | 垃圾分选 目标检测 多尺度感受视野 注意力机制 |
| 收稿时间： | 2021-01-17 |
A SINGLE-STAGE OBJECT DETECTION METHOD FOR DOMESTIC GARBAGE BASED ON MULTI-SCALE RECEPTIVE FIELD ATTENTION MECHANISM |
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| Affiliation: | 1. College of Electrical Engineering, Xinjiang University, Urumqi 830047, China;2. Intelligent Manufacturing Technology and System Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
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| Abstract: | In order to improve the detection accuracy of multi-scale and different materials and ensure the robustness of waste classification, based on the existing deep convolution neural network and single-stage target detection algorithm YOLOv3, an ECA with multi-scale perception visual field attention mechanism was proposed as ECA_ ERFB_ S-YOLOv3 algorithm. The multi-scale perceptual field module was introduced in front of the algorithm detector, so that the algorithm could select the appropriate perceptual field to match the garbage objects with different scales, and the detection accuracy was improved; then, ResNet50 was used to replace the original skeleton network Darknet53. Under the condition of transfer learning, efficient attention mechanism was used to autonomously enhance and suppress the features in ResNet50 and multi-scale sensory visual field module, which improved the robustness of the algorithm. Finally, K-means algorithm was used to regress the anchor box, and the allocation method of anchor box was designed. The results of ablation experiment showed that ECA_ ERFB_ S-YOLOV3 had higher precision and better robustness; when detecting densely stacked domestic waste, the algorithm could better meet the needs of the task and show better detection effect. |
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