A custodian exists at the entrance to detect prohibited items when entering the library, and if a person detects them, it is difficult and inefficient to identify all people who use prohibited items from entering the library. To this end, an automatic system is built to automatically detect, record, and warn off prohibited items.
전체 코드는 이 주소에서 확인 가능합니다. https://github.com/Growth17-Graduation-Project/AUTO.DETECT
yolo 논문 https://arxiv.org/pdf/1506.02640.pdf
yolo 논문 리뷰 https://bkshin.tistory.com/entry/%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0-YOLOYou-Only-Look-Once
yolov5 가이드라인 https://towardsai.net/p/computer-vision/yolo-v5-object-detection-on-a-custom-dataset
yolov5 가이드라인 https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/
yolo 가이드라인 https://www.youtube.com/watch?v=yJwDW1WxNZ8&list=PLP1pGkh-tN1B0fcosHdZLWZkhhnEB6xUm&index=1
yolo 구조 파악 https://wiserloner.tistory.com/1247
TestData_1 (BCCD) https://github.com/Shenggan/BCCD_Dataset/tree/master/BCCD
TestData_2 (COCO) https://public.roboflow.com/object-detection/microsoft-coco-subset
WEB Front - https://github.com/JangBomi/Object_Detection_Front
WEB API - https://github.com/JangBomi/Object_Detection_API
Convert darknet weights to tensorflow and run - https://github.com/hunglc007/tensorflow-yolov4-tflite (https://github.com/hunglc007/tensorflow-yolov4-tflite/blob/master/LICENSE)
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