Object Detection in Low-Light Conditions: Analysis of Algorithms

Object detection is a fundamental task in computer vision that involves detecting and localizing objects in an image or video. However, object detection can be challenging in low-light conditions, where images can be noisy, blurry, and lack contrast. In this article, we will provide a comparative analysis of algorithms for object detection in low-light conditions, including traditional computer vision techniques and deep learning models.

Fig-1.1: Object Detection in Low Lightening Area

In this article, we will explore the best techniques that can be useful in low-lighting conditions for performing object detection.

Traditional Computer Vision Techniques (for Low-Lightening Conditions)

Traditional computer vision techniques for object detection in low-light conditions often involve enhancing the image to improve visibility and contrast, followed by applying classical computer vision algorithms to detect objects. Some of the most commonly used techniques include:

• Histogram equalization: Histogram equalization is a popular technique for image enhancement that aims to improve the contrast of an image by stretching its histogram across the entire intensity range. In low-light conditions, images may have a narrow histogram with limited contrast, making it difficult to detect objects. Histogram equalization can…