VERGNet: Visual Enhancement Guided Robotic Grasp Detection under Low-Light Condition

Although existing grasp detection methods have achieved encouraging performance under well-light conditions, repetitive experiments have found that the detection perfor- mance would deteriorate drastically under low-light conditions. Although supplementary information can be provided by addi- tional sensors, such as depth camera, the sparse and weak visual features still hinder the improvement of detection accuracy. In order to address these, we propose a visual enhancement guided grasp detection model (VERGNet) to improve the robustness of robotic grasping in low-light conditions. Firstly, a simul- taneous grasp detection and low-light feature enhancement framework is designed, which integrates residual blocks with coordinate attention to re-optimize grasping features. Then, the unsupervised low-light feature enhancement strategy is adopted to reduce the dependence on paired data as well as improve the algorithmic robustness to low-light conditions. Extensive experiments are finally conducted on two newly-constructed low-light grasp datasets and the proposed method achieves 98.9% and 91.2% detection accuracy respectively, which are superior to comparative methods. Besides, the effectiveness in our method has also been validated in real-world low-light imaging scenarios.