# Focal Loss Dense Detector for Vehicle Surveillance

arXiv: https://arxiv.org/abs/1803.01114 · HTML: https://mlsanity.com/paper/1803.01114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss](https://mlsanity.com/paper/2105.06345/) (2021)

## Abstract (arXiv)

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.