Jia Lei, Wang Jianzhu, Wang Tianyuan, Li Xiaobao, Yu Haomin, Li Qingyong
Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China.
Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518000, China.
Entropy (Basel). 2022 Mar 28;24(4):466. doi: 10.3390/e24040466.
Vehicles carrying hazardous material (hazmat) are severe threats to the safety of highway transportation, and a model that can automatically recognize hazmat markers installed or attached on vehicles is essential for intelligent management systems. However, there is still no public dataset for benchmarking the task of hazmat marker detection. To this end, this paper releases a large-scale vehicle hazmat marker dataset named VisInt-VHM, which includes 10,000 images with a total of 20,023 hazmat markers captured under different environmental conditions from a real-world highway. Meanwhile, we provide an compact hazmat marker detection network named HMD-Net, which utilizes a revised lightweight backbone and is further compressed by channel pruning. As a consequence, the trained-model can be efficiently deployed on a resource-restricted edge device. Experimental results demonstrate that compared with some established methods such as YOLOv3, YOLOv4, their lightweight versions and popular lightweight models, HMD-Net can achieve a better trade-off between the detection accuracy and the inference speed.
运输危险材料（危险品）的车辆对公路运输安全构成严重威胁，对于智能管理系统而言，一个能够自动识别安装或附着在车辆上的危险品标识的模型至关重要。然而，目前仍没有用于危险品标识检测任务基准测试的公开数据集。为此，本文发布了一个名为VisInt-VHM的大规模车辆危险品标识数据集，该数据集包含10000张图像，共计20023个危险品标识，这些标识是在不同环境条件下从真实世界的高速公路上采集的。同时，我们提供了一个名为HMD-Net的紧凑型危险品标识检测网络，该网络采用了经过改进的轻量级主干，并通过通道剪枝进一步压缩。因此，训练好的模型可以在资源受限的边缘设备上高效部署。实验结果表明，与一些已有的方法如YOLOv3、YOLOv4、它们的轻量级版本以及流行的轻量级模型相比，HMD-Net能够在检测精度和推理速度之间取得更好的平衡。
