RGB-D visual saliency detection of stacked fruits under poor lighting

The saliency detection of the same kind of stacked fruits can assist robots in completing sorting tasks, which is an important prerequisite for the grading and packing of fruits. In order to accurately obtain saliency targets of fruits in the same kind of stacked state under overexposure, non-uniform illumination, and low illumination, a method for detecting stacked fruits under poor illumination based on RGB-D visual saliency was proposed. Based on the Res2Net network, features from each layer of two images were obtained. To realize the complementary advantages between RGB features and depth features, the input RGB images were preprocessed using depth weighting to obtain purified RGB features. To increase the information interaction between branches of different scales and better balance the fusion features and modal exclusive features, a multi-scale progressive fusion module was proposed. To minimize the difference between the initial saliency maps generated by different features and improve the accuracy of the final predicted saliency maps, a multi-branch hybrid supervised method was used. The comprehensive experiments on the self-made dataset of the same kind of stacked fruits show that the proposed algorithm is superior to five state-of-the-art RGB-D SOD methods in four key indicators: S value, F value, and MAE value, which are 0.979, 0.992, and 0.006, respectively, and the P-R curve, which is also closer to the upper right corner of the graph. These values demonstrate that the proposed algorithm can accurately obtain saliency targets in the same kind of stacked fruits. The results of this study can promote the automatic development of the fruit production and packaging industry.