Chao Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Chao Xu has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Hua Zhao
College of Engineering, Hebei Normal University, Shijiazhuang 050024, People’s Republic of China
1 shared publication - Jiaxing Chen
School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, China
1 shared publication - Xiang Wang
College of Engineering, Hebei Normal University, Shijiazhuang 050024, Hebei, People’s Republic of China
1 shared publication - Zhexian Zhang
School of Engineering, Hebei Normal University, Shijiazhuang 050024, China
1 shared publication
Latest Publications
- Open Access
BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection
Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort and is suitable for edge devices. First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. Secondly, a global and local feature fusion module for small targets (BIG) is integrated into the neck network to preserve more feature information, reduce error information in higher-level features, and increase the model’s effectiveness in detecting small targets. Finally, to prevent the detection accuracy impact due to excessive lightweighting, the lightweight shared head (LSH) is constructed. The reparameterization technique further improves detection accuracy without additional parameters and computational cost. Experimental results of BGLE-YOLO on the underwater datasets DUO (Detection Underwater Objects) and RUOD (Real-World Underwater Object Detection) show that the model achieves the same accuracy as the benchmark model with an ultra-low computational cost of 6.2 GFLOPs and an ultra-low model parameter of 1.6 MB.
Open AccessBGLE-YOLO: A Lightweight Model for Underwater Bio-Detection
Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort and is suitable for edge devices. First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. Secondly, a global and local feature fusion module for small targets (BIG) is integrated into the neck network to preserve more feature information, reduce error information in higher-level features, and increase the model’s effectiveness in detecting small targets. Finally, to prevent the detection accuracy impact due to excessive lightweighting, the lightweight shared head (LSH) is constructed. The reparameterization technique further improves detection accuracy without additional parameters and computational cost. Experimental results of BGLE-YOLO on the underwater datasets DUO (Detection Underwater Objects) and RUOD (Real-World Underwater Object Detection) show that the model achieves the same accuracy as the benchmark model with an ultra-low computational cost of 6.2 GFLOPs and an ultra-low model parameter of 1.6 MB.