Real‐time detection of Angelica dahurica tablet using YOLOX_am

Abstract In the production of Angelica dahurica tablet (ADT), the manual sorting approach often leads to inefficiency, inconsistent standards, and subjective grading results. The traditional machine vision‐based sorting method, while helping to reduce the demand for labor in factories, suffers from problems such as incomplete contour detection and poor classification of dahurica tablets. To address the above problems, this paper proposes YOLOX_am, a novel deep learning‐based network that combines the fast detection ability of YOLOX and the feature weighting ability of the attention mechanism. In addition, a real‐time sorting system for dahurica tablets is also built and YOLOX_am is deployed in it. The experimental results show that the mAP of ADT's detection using the proposed model reaches 83.53%, which outperforms the original network YOLOX by 4.88%. Moreover, the detection speed of YOLOX_am reaches 1390 ms per image, which meets the requirement of real‐time sorting. Therefore, the combination of YOLOX and the attention mechanism is feasible and effective. YOLOX_am is both fast and accurate for ADT's detection and can be deployed in the sorting system to meet actual production needs. Practical applications The traditional classification of defects in herbal tablets mainly relies on manual detection. This paper introduces a deep learning method into the sorting of herbal tablets. To solve the problem of subtle differentiation of tablets' defects, a neural network structure that can quickly and accurately detect small defective samples is proposed. This network has been applied to practical production and achieved high effectiveness.