Focal Loss Dense Detector for Vehicle Surveillance
Xiaoliang Wang, Peng Cheng, Xinchuan Liu, Benedict Uzochukwu

TL;DR
This paper introduces a focal loss-based RetinaNet for vehicle detection that achieves a balance of high speed and accuracy, outperforming traditional two-stage detectors on the DETRAC dataset.
Contribution
It presents a novel application of focal loss in RetinaNet to enhance one-stage detector accuracy for vehicle surveillance.
Findings
Achieves state-of-the-art accuracy on DETRAC dataset.
Balances speed and detection performance effectively.
Outperforms traditional two-stage detectors in vehicle detection.
Abstract
Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Visual Attention and Saliency Detection
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Convolution · 1x1 Convolution · Feature Pyramid Network · RetinaNet · Focal Loss
