Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model

Abstract

Litchi is often harvested by clamping and cutting the branches, which are small and can easily be damaged by the picking robot. Therefore, the detection of litchi branches is particularly significant. In this article, an fully convolutional neural network-based semantic segmentation algorithm is proposed to semantically segment the litchi branches. First, the DeepLabV3+ semantic segmentation model is combined with the Xception depth separable convolution feature. Second, transfer learning and data enhancement are used to accelerate the convergence and improve the robustness of the model. Third, a coding and a decoding structure are adopted to reduce the number of network parameters. The decoding structure uses upsampling and the shallow features to fuse, and the same weight is assigned to ensure that the shallow feature semantics and the deep feature semantics are evenly distributed. Fourth, using atrous spatial pyramid pooling, we can better extract the semantic pixel position information without increasing the number of weight parameters. Finally, different sizes of hole convolution are used to ensure the prediction accuracy of small targets. Experiment results demonstrated that the DeepLabV3+ model using the Xception_65 feature extraction network obtained the best results, achieving a mean intersection over union (MIoU) of 0.765, which is 0.144 higher than the MIoU of 0.621 of the original DeepLabV3+ model. Meanwhile, the DeepLabV3+ model using the Xception_65 network has greater robustness, far exceeding the PSPNet_101 and ICNet in detection accuracy. The aforementioned results indicated that the proposed model produced better detection results. It can provide powerful technical support for the gripper picking robot to find fruit branches and provide a new solution for the problem of aim detection and recognition in agricultural automation.