TY - JOUR ID - 106905 TI - Comparative analysis on YOLO object detection with OpenCV JO - International Journal of Research in Industrial Engineering JA - RIEJ LA - en SN - 2783-1337 AU - Deshpande, H. AU - Singh, A. AU - Herunde, H. AD - Department of Computer Application, Jain (Deemed to-be) University, Bengaluru, Karnataka, India. Y1 - 2020 PY - 2020 VL - 9 IS - 1 SP - 46 EP - 64 KW - YOLO KW - Faster-RCNN KW - convolutional neural network KW - COCO DO - 10.22105/riej.2020.226863.1130 N2 - Computer Vision is a field of study that helps to develop techniques to identify images and displays. It has various features like image recognition, object detection and image creation, etc. Object detection is used for face detection, vehicle detection, web images, and safety systems. Its algorithms are Region-based Convolutional Neural Networks (RCNN), Faster-RCNN and You Only Look Once Method (YOLO) that have shown state-of-the-art performance. Of these, YOLO is better in speed compared to accuracy. It has efficient object detection without compromising on performance. UR - https://www.riejournal.com/article_106905.html L1 - https://www.riejournal.com/article_106905_afd0caf26202eb3ac3b605fd17894255.pdf ER -