Detecting real-time objects for traffic surveillance system using single shot detector over CNN with improved accuracy

Citations Over Time

Abstract

Real-Time Object Detection in traffic surveillance is one of the latest topics in today's world for detecting the objects using Single Shot Detector algorithm over Convolutional Neural Networks. Methods and Materials: Real-Time Object Detection is performed using Single Shot Detector (N=40) over Convolutional Neural Networks (N=40) with the split size of training and testing dataset 70% and 30% respectively. Results: Single Shot Detector has significantly better accuracy (78.9%) compared to Convolutional Neural Networks (47.7%) and attained significance value of p = 0.125. Conclusion: Single Shot Detector achieved significantly better object detection than Convolutional Neural Networks for identifying real-time objects in traffic surveillance.

Related Papers

• → Birdman"s Single Shot Effect : Realist Aesthetics and the Illusion(2016)
• One Shot Device의 저장 신뢰도 분석에 관한 연구(2016)
• Birdman's Single Shot Effect: Realist Aesthetics and the Illusion(2016)
• I've heard that there's a high-dose flu shot available. Is it better for older adults than the regular flu shot? Should I ask for the high-dose shot?(2011)
• → Tomography of a single-atom-resolved detector in the presence of shot-to-shot number fluctuations(2024)