Real-Time Obstacle Detection Based on Image Semantic Segmentation and Fusion Network

Abstract

During fruit production, the robots must walk stably across the orchard, and detect the obstacles in real time on its path. With the rapid process of deep convolutional neural network (CNN), it is now a hot topic to enable orchard robots to detect obstacles through image semantic segmentation. However, most such obstacle detection schemes are under performing in the complex environment of orchards. To solve the problem, this paper proposes an image semantic fusion network for real-time detection of small obstacles. Two branches were set up to extract features from red-green-blue (RGB) image and depth image, respectively. The information extracted by different modules were merged to complement the image features. The proposed network can operate rapidly, and support the real-time detection of obstacles by orchard robots. Experiments on orchard scenarios show that our network is superior to the latest image semantic segmentation methods, highly accurate in the recognition of high-definition images, and extremely fast in reasoning.