Research on Target Detection Technology of Industrial Robot Based on Deep Learning

Traditional manual sorting tasks are inefficient, prone to fatigue, and have a high error rate. With the rise of computer vision technology, industrial robots equipped with various visual detection algorithms are increasingly used for target classification and positioning, thus completing sorting operations. However, these detection algorithms often suffer from slow detection speeds and poor performance in detecting overlapping or occluded targets, failing to meet the technical demands of industrial production, which is moving towards large-scale, high-efficiency, and unmanned operations. This paper focuses on achieving real-time and accurate target recognition and efficient, stable sorting tasks by conducting research on deep learning-based target detection and sorting technologies for industrial robots.