基于改进YOLOv3的集卡车头防砸检测
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Anti-crash Detection of Container Truck Head Based on Improved YOLOv3
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    摘要:

    在自动化港口集装箱起重机作业流程中, 集卡车头防砸检测是不可或缺的一个环节. 针对在此环节采用人工确认方法效率低和基于激光扫描方法耗费高、系统复杂的问题, 本文提出一种基于作业场景视频图像和深度学习的算法对集卡车头进行目标检测. 建立集卡车头样本数据集, 采用DCTH-YOLOv3检测模型, 通过模型迁移学习方法进行样本训练. DCTH-YOLOv3模型是本文提出的一种改进YOLOv3算法模型, 该算法改进了YOLOv3的FPN结构提出一种新的特征金字塔结构—AF_FPN, 在高、低阶特征融合时通过引入具有注意力机制的AFF模块聚焦有效特征、抑制干扰噪声, 提高了检测精度. 另外, 使用CIoU loss度量损失替代L2损失, 提供更加准确的边界框变化信息, 模型检测精度得到进一步提升. 实验结果表明: DCTH-YOLOv3算法在GTX1080TI上检测速率可达46 fps, 相比YOLOv3算法仅降低了3 fps; 检测精度AP0.50.9974、AP0.90.4897, 其中AP0.9相比YOLOv3算法提升了16.4%. 本研究算法相比YOLOv3算法, 精度更高, 更能满足自动化作业对集卡防砸检测高精度、快识别的要求.

    Abstract:

    In the process of automated crane operations for port containers, the detection of container truck heads is an indispensable link. To solve the problem of low efficiency by manual confirmation and high costs and complex systems by the laser scanning method, this study proposes an algorithm based on video images of operation scenes and deep learning for target detection of container truck heads. Specifically, upon the construction of a sample data set of container truck heads, the DCTH-YOLOv3 detection model is used, and sample training is performed through the method of model migration learning. The DCTH-YOLOv3 model is an improved YOLOv3 model proposed in this study. The algorithm improves the FPN structure of YOLOv3 and proposes a new feature pyramid structure—AF_FPN. During the fusion of higher- and lower-order features, the AFF module with the attention mechanism is introduced to focus on effective features and suppress interference noise, which increases the accuracy of detection. In addition, the metric CIoU loss is used to replace L2 loss to provide more accurate boundary box change information and further improve the model detection accuracy. The experimental results indicate that the detection rate of DCTH-YOLOv3 can reach 46 fps on GTX1080TI, which is only 3 fps lower than that of YOLOv3. The detection accuracy can reach AP0.5 0.9974 and AP0.9 0.4897, in which AP0.9 is 16.4% higher than that of YOLOv3. Compared with the YOLOv3 algorithm, the proposed algorithm has higher accuracy and can better meet the requirements of automatic operations for high accuracy and fast identification in the anti-collision detection of container trucks.

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张柏阳,赵霞,包启睿.基于改进YOLOv3的集卡车头防砸检测.计算机系统应用,2023,32(2):190-198

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  • 收稿日期:2022-06-14
  • 最后修改日期:2022-07-12
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  • 在线发布日期: 2022-09-26
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