Bing Jing
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Bing Jing has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Lu Chen
School of Vehicle and Mobility, Tsinghua University, Beijing 100190, China
1 shared publication - Pianzhang Duan
School of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China
1 shared publication - Yanhui Du
School of Information and Network Security, People’s Public Security University of China, Beijing 100038, China
1 shared publication
Latest Publications
- Open Access
EM-YOLO: An X-ray Prohibited-Item-Detection Method Based on Edge and Material Information Fusion
Using X-ray imaging in security inspections is common for the detection of objects. X-ray security images have strong texture and RGB features as well as the characteristics of background clutter and object overlap, which makes X-ray imaging very different from other real-world imaging methods. To better detect prohibited items in security X-ray images with these characteristics, we propose EM-YOLOv7, which is composed of both an edge feature extractor (EFE) and a material feature extractor (MFE). We used the Soft-WIoU NMS method to solve the problem of object overlap. To better extract features, the attention mechanism CBAM was added to the backbone. According to the results of several experiments on the SIXray dataset, our EM-YOLOv7 method can better complete prohibited-item-detection tasks during security inspection with detection accuracy that is 4% and 0.9% higher than that of YOLOv5 and YOLOv7, respectively, and other SOTA models.
Open AccessEM-YOLO: An X-ray Prohibited-Item-Detection Method Based on Edge and Material Information Fusion
Using X-ray imaging in security inspections is common for the detection of objects. X-ray security images have strong texture and RGB features as well as the characteristics of background clutter and object overlap, which makes X-ray imaging very different from other real-world imaging methods. To better detect prohibited items in security X-ray images with these characteristics, we propose EM-YOLOv7, which is composed of both an edge feature extractor (EFE) and a material feature extractor (MFE). We used the Soft-WIoU NMS method to solve the problem of object overlap. To better extract features, the attention mechanism CBAM was added to the backbone. According to the results of several experiments on the SIXray dataset, our EM-YOLOv7 method can better complete prohibited-item-detection tasks during security inspection with detection accuracy that is 4% and 0.9% higher than that of YOLOv5 and YOLOv7, respectively, and other SOTA models.