A Lightweight Blind Obstacle Detection Network for Mobile Side

Abstract

China has the longest and widest distribution of blind corridors in the world, but in many cities they are virtually non-existent, with all kinds of obstacles affecting the movement of the blind. Thus, guaranteeing safe traveling for the blind has become an increasingly important topic. Some existing assistive traveling devices for the blind have problems such as poor portability and low-cost performance. With the rise of the mobility era and the rapid development of deep learning technology, the target detection of blind obstacles on cell phones has become feasible. In this paper, we take the target detection model YOLOv8n as the base network, redesign the neck of the network, use GS convolution as the basis, adopt one-time aggregation and cross-channel branching to build a lightweight module, and use the CARAFE operator as the up-sampling method, and stack the lightweight module with CARAFE operator to build a new feature fusion layer, forming a lightweight feature-aware enhanced target detection network. The results show that the accuracy still reaches 99.5% of the original network under the premise that the model size is reduced by 1.2%, and the improved network achieves a balance between accuracy and lightweight.