Seatbelt detection is an important topic in intelligent transport systems. The accuracy of seatbelt detection in traffic video surveillance is affected by many factors, such as complex road environments, lighting, weather, direction of the camera, etc., which bring difficulties for traditional image processing methods. We propose a seatbelt detection method based on classification using convolutional neural networks. Dense residual blocks are used to avoid gradient dispersion and information loss during the network training. A two-level attention mechanism is also introduced to further improve the network performance by simultaneously modeling the correlation among different feature channels using channel attention mechanism, and correlation among different pixels within the feature map using pixel attention mechanism. To make the whole system complete, we added the driver area localization module, which is accomplished using the YOLOV3 model. Small images concentrated on the driver location, which are obtained through the localization operation from larger images, are passed into the classification network for seatbelt detection. We compared the proposed method with several other methods. Comparative results show that the proposed method has higher accuracy and is more robust for seatbelt detection for complex environments.

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基于改进的两级注意力机制密集残差网络的道路监控图像安全带检测

安全带检测是智能交通系统中的一个重要课题。交通视频监控中安全带检测的准确性受复杂道路环境、光照、天气、摄像头方向等多种因素影响，给传统的图像处理方法带来了困难。我们提出了一种基于使用卷积神经网络分类的安全带检测方法。密集残差块用于避免网络训练过程中的梯度分散和信息丢失。还引入了两级注意力机制，通过使用通道注意力机制同时建模不同特征通道之间的相关性，以及使用像素注意力机制在特征图中不同像素之间的相关性，进一步提高网络性能。为了使整个系统更加完整，我们增加了驾驶员区域定位模块，该模块使用YOLOV3模型完成。通过定位操作从较大的图像中获得的集中在驾驶员位置上的小图像被传递到分类网络中进行安全带检测。我们将所提出的方法与其他几种方法进行了比较。对比结果表明，所提出的方法具有更高的准确性，对于复杂环境下的安全带检测具有更强的鲁棒性。我们将所提出的方法与其他几种方法进行了比较。对比结果表明，所提出的方法具有更高的准确性，对于复杂环境下的安全带检测具有更强的鲁棒性。我们将所提出的方法与其他几种方法进行了比较。对比结果表明，所提出的方法具有更高的准确性，并且对于复杂环境下的安全带检测具有更强的鲁棒性。