Xiaobao Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaobao Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Haomin Yu
Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
1 shared publication - Jianzhu Wang
School of Qilu Transportation, Shandong University, Jinan 250061, China
1 shared publication - Lei Jia
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
1 shared publication - Qingyong Li
Beijing Jiaotong University
1 shared publication - Tianyuan Wang
Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518000, China
1 shared publication
Latest Publications
- Open Access
HMD-Net: A Vehicle Hazmat Marker Detection Benchmark
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.
Open AccessHMD-Net: A Vehicle Hazmat Marker Detection Benchmark
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.