Improved intelligent image segmentation algorithm for mechanical sensors in industrial IoT: A joint learning approach

Abstract

The industrial Internet of Things (IoT) can monitor production in real-time by collecting the status of parts on the production line with cameras. It is easy to have bright and dark areas in the same image because of the smooth surfaces of mechanical parts and the unstable light source, which affects semantic segmentation’s performance. This paper proposes a joint learning method to eliminate the influence of illumination on semantic segmentation. Semantic image segmentation and image decomposition are jointly trained in the same model, and the reflectance image is used to guide the semantic segmentation task without the illumination component. Moreover, this paper adopts an enhanced convolution kernel to improve the pixel accuracy and BN fusion to enhance the inference speed, optimizing the model to meet real-time detection needs. In the experiments, a dataset of real gear parts was collected from industrial IoT cameras. The experimental results show that the proposed joint learning approach outperforms the state-of-the-art methods in the task of edge mechanical part detection, with about 4% pixel accuracy improvement.