Comparison Analysis and Case Study for Deep Learning-based Object Detection Algorithm

Background/Objectives Deep learning which main technology in AI has high growth with being applied to field of speech recognition and Image classification.Especially, Deep learning technology in the field of Image classification is being applied as a core technology to Self-driving and crime prevention monitoring system that is recently emerging as the future industry.Methods/Statistical analysis: Various algorithm which is improved and developed CNN being able to do image process is suggested as Deep learning model in image recognition field.In this paper, we introduce various object detection algorithm including CNN.And explore most representative algorithms just R-CNN, Fast R-CNN, Faster R-CNN and difference between versions of YOLO devised to detect and track in real time.Findings: This paper evaluates deep learning algorithm's performance by comparative analysis about mAP (mean average precision) and FPS (frames per second).In result of performance evaluation, YOLO algorithm is confirmed as that It shows excellent result in speed that detects and recognizes object and accuracy in real time system environment.Finally, we search cases in field of autonomous driving and access control system and home anti-crime system.Improvements/Applications: In this research, we can understand object detection algorithm among speech recognition technologies and proper field in each algorithm, apply security service based on image, recommend proper algorithm in various environment just like autonomous driving and security work, etc.