The Low Computation and Real-Time Shoe Detection with Timestamp for Production Tracking in Shoe Manufacturing

Abstract

Production planners could not get the update on the actual number of products in real-time. They do not realize the unmatched production until a few days later. Thus, the planners need to revise their production plan with reserve capacity for this unmatched production, and it causes manufacturing to waste a lot of time and money. The production outcome is usually manually counted at the end of the day and recorded on paper. This paper proposes an image processing system for counting products with a timestamp. The YOLOv4-tiny and DNN-OpenCV are utilized to detect an object. The detected object will be counted using the intersection detection and tesseract engine to extract time from the video. The object detection is trained using the 10 folds technique with 106 object photos. The proposed approach is tested with 8 videos for counting accuracy and timestamp accuracy. The testing result reveals that our proposed method achieves 100% of object counting and timestamp accuracy of 80 % compared with the manual counting with the timestamp. The proposed technique is suitable for counting objects with timestamps in real-time.