AFC-ResNet18: A Novel Real-Time Image Semantic Segmentation Network for Orchard Scene Understanding

Abstract

Highlights A novel real-time image semantic segmentation network for orchards, termed AFC-ResNet18, was designed and tested. The AFC-ResNet18 model outperformed the SwiftNet network in terms of segmentation depth. The AFC-ResNet18 model achieved the highest accuracy in the architecture performance testing. The AFC-ResNet18 model won first place in the orchard scene test with 72.5% accuracy. Abstract. Semantic segmentation is a fundamental prerequisite for the real-time understanding of scenes. This understanding is essential for developing automated devices that can enhance productivity. Orchards, being labor-intensive and time-consuming workplaces, urgently require automated equipment to boost efficiency. Therefore, the objective of this article is to develop a real-time image semantic segmentation network tailored for orchard environments. This development aims to offer significant new insights into the design of automated maintenance and harvesting equipment. Based on ResNet, the 2015 classification champion network, a novel real-time image semantic segmentation network termed AFC-ResNet18, which used an attentional feature complementary module (AFC) to fuse RGB and depth image information, was designed and systematically tested. Interestingly, in the segmentation ability tests, the AFC-ResNet18 model outperformed the SwiftNet network in terms of segmentation depth. Surprisingly, in the architecture performance testing, the AFC-ResNet18 model achieved the highest accuracy. Noteworthily, in the orchard scene test, the AFC-ResNet18 model won first place with 72.5% accuracy. Predictably, these findings may accelerate the development of novel automated equipment to maintain the orchard worldwide, especially AFC-ResNet18 based robots. Keywords: Attentional feature complementary module, Orchard, Real-time, Robots, Semantic segmentation.