YOLO (You Only Look Once)

App for object detection :

Reference

• Darknet

• YOLO clarification

deep learning framework to run YOLO algorythm

1. Darknet : it’s the framework built from the developer of YOLO and made specifically for yolo. Advantage: it’s fast, it can work with GPU or CPU Disadvantage: it olny works with Linux os
2. Darkflow: it’s the adaptation of darknet to Tensorflow (another deep leanring framework). Advantage: it’s fast, it can work with GPU or CPU, and it’s also compatible with Linux, Windows and Mac. Disadvantage: the installation it’s really complex, especially on windows
3. Opencv: also opencv has a deep learning framework that works with YOLO. Just make sure you have opencv 3.4.2 at least. Advantage: it works without needing to install anything except opencv. Disadvantage: it only works with CPU, so you can’t get really high speed to process videos in real time.

weight file: trained model. cfg file: configuration file where settings of the algorythm are presented name files: name of the objets that the alorythm can detect.

YOLO accepts three sizes of image: Blob is used to extract features fromt he iamge and to resize them. 320×320 it’s small so less accuracy but better speed 609×609 it’s bigger so high accuracy and slow speed 416×416 it’s in the middle and you get a bit of both.

• Performance Metrics

Intersection over Union (IoU)

mean average precision (mAP)

• 객체 커스텀

• 커스텀 모델 정확도를 높이는 방법 클래스당 2000개 이상의 이미지가 필요. (at least 100 images recommended) 이미지 속 객체들의 크기, 밝기, 위치, 회전, 배경이 다양할 수록 정확도가 높아집니다. 검출하지 않으려는 객체들의 사진도 필요합니다. (이 사진들은 빈 txt 파일을 가져야 합니다.) roi 파일이 없는 이미지들의 roi를 파일을 만들어 주는 파이썬 코드를 첨부합니다.

• Annotation tool used: LabelImg