Aiping Liu
Aiping Liu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Changsheng Zhu
College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
1 shared publication - Chenbo Shi
College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
1 shared publication - Chun Zhang
College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
1 shared publication - Delin Wang
College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
1 shared publication - Xiangyu Zhang
College of lntelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
1 shared publication - Xiaobing Feng
Beijing Botsing Technology Co., Ltd., Beijing 100176, China
1 shared publication
Latest Publications
- Open Access
RICNET: Retinex-Inspired Illumination Curve Estimation for Low-Light Enhancement in Industrial Welding Scenes
Feature tracking is essential for welding crawler robots’ trajectory planning. As welding often occurs in dark environments like pipelines or ship hulls, the system requires low-light image capture for laser tracking. However, such images typically have poor brightness and contrast, degrading both weld seam feature extraction and trajectory anomaly detection accuracy. To address this, we propose a Retinex-based low-light enhancement network tailored for cladding scenarios. The network features an illumination curve estimation module and requires no paired or unpaired reference images during training, alleviating the need for cladding-specific datasets. It adaptively adjusts brightness, restores image details, and effectively suppresses noise. Extensive experiments on public (LOLv1 and LOLv2) and self-collected weld datasets show that our method outperformed existing approaches in PSNR, SSIM, and LPIPS. Additionally, weld seam segmentation under low-light conditions achieved 95.1% IoU and 98.9% accuracy, confirming the method’s effectiveness for downstream tasks in robotic welding.
Open AccessRICNET: Retinex-Inspired Illumination Curve Estimation for Low-Light Enhancement in Industrial Welding Scenes
Feature tracking is essential for welding crawler robots’ trajectory planning. As welding often occurs in dark environments like pipelines or ship hulls, the system requires low-light image capture for laser tracking. However, such images typically have poor brightness and contrast, degrading both weld seam feature extraction and trajectory anomaly detection accuracy. To address this, we propose a Retinex-based low-light enhancement network tailored for cladding scenarios. The network features an illumination curve estimation module and requires no paired or unpaired reference images during training, alleviating the need for cladding-specific datasets. It adaptively adjusts brightness, restores image details, and effectively suppresses noise. Extensive experiments on public (LOLv1 and LOLv2) and self-collected weld datasets show that our method outperformed existing approaches in PSNR, SSIM, and LPIPS. Additionally, weld seam segmentation under low-light conditions achieved 95.1% IoU and 98.9% accuracy, confirming the method’s effectiveness for downstream tasks in robotic welding.