SPANet—Sparse Convolutional Pyramid Attention Network for Grasping Detection in Low-Light Conditions

This paper presents a novel grasping detection network - the Sparse Convolutional Pyramid Attention Network (SPANet), designed to efficiently detect grasping in low-light conditions. The proposed network is particularly suitable for performing grasping tasks in low-light environments, including dimming enhancement and object detection. Furthermore, we propose a method based on inverse curve adjustment, which simulates low-light image algorithms with controllable dimming levels. The SPANet was trained and tested on the Cornell and Jacquard datasets, achieving detection accuracies of 94.38% and 90.76%, respectively. Moreover, we tested the SPANet on the Yuejiang CR5 robot in actual low-light environments, achieving a grasping success rate of 92.69 %. These results demonstrate the potential of the proposed approach to provide reliable and efficient solutions for robots operating in low-light environments.