Exploring Low-light Object Detection Techniques

TL;DR

This paper investigates the effectiveness of various image enhancement techniques and object detection models under low-light conditions, aiming to improve detection accuracy despite challenging image quality.

Contribution

It systematically compares enhancement algorithms and detection models for low-light images, providing insights into optimal combinations for better object detection.

Findings

Histogram equalization and unpaired image translation improve detection accuracy.

Certain detection models outperform others on enhanced low-light images.

The study offers future directions for low-light object detection research.

Abstract

Images acquired by computer vision systems under low light conditions have multiple characteristics like high noise, lousy illumination, reflectance, and bad contrast, which make object detection tasks difficult. Much work has been done to enhance images using various pixel manipulation techniques, as well as deep neural networks - some focused on improving the illumination, while some on reducing the noise. Similarly, considerable research has been done in object detection neural network models. In our work, we break down the problem into two phases: 1)First, we explore which image enhancement algorithm is more suited for object detection tasks, where accurate feature retrieval is more important than good image quality. Specifically, we look at basic histogram equalization techniques and unpaired image translation techniques. 2)In the second phase, we explore different object detection models that can be applied to the enhanced image. We conclude by comparing all results, calculating mean average precisions (mAP), and giving some directions for future work.