Development of a core feature identification application based on the Faster R-CNN algorithm. (October 2022)

• Record Type: Journal Article
• Authors: Jiang, Quan; Jia, Mingtao; Bi, Lin; Zhuang, Zheng; Gao, Kaixin
• Is Part Of: Engineering applications of artificial intelligence. Volume 115(2022)
• Publication Date: 2022-10
• Subjects: Core feature identification -- Application development -- Deep learning -- Faster R-CNN; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105200
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: As engineering rock mass quality assessment is an important part of the evaluation of deposit mining technical conditions, the identification and counting of core features are essential but time-consuming, and the complexity of core features leads to the invalidity of traditional image processing programs. In such a case, we developed an efficient automated core feature identification and counting application by adopting the Faster R-CNN algorithm together with a self-designed batch processing and counting program, which allows for the high-speed identification of target features among many pictures and can count and output formatted identification results. The evaluation results show that this application can significantly improve the identification accuracy and speed up the process with the help of a deep learning algorithm and our computer program. In the comparison and selection of the Faster R-CNN and YOLO algorithms, YOLO was eliminated due to poor performance. The main reason is that the multiscale self-similarity of core features has adverse effects on the multiscale segmentation method of the YOLO algorithm, making YOLO identify one feature repeatedly. The overall training evaluation F1-score of the Faster R-CNN-based AI model reaches 0.91, showing an ideal result. In the practical test, the overall AI identification F1-score is 0.93, and the application processing F1-score reaches 0.92. The time complexities of the AI model and application are both acceptable, with T(n) = O(n). In terms of identification speed, the application process is 48 times faster than that of manual identification.