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 help to improve the contrast of the image and make objects more visible.
• Laplacian of Gaussian (LoG): The Laplacian of Gaussian (LoG) is a filter that can be used to detect edges and features in an image. The LoG filter applies a Gaussian smoothing filter to an image, followed by computing the Laplacian of the resulting smoothed image. The LoG filter can help to enhance edges and features in an image, making it easier to detect objects in low-light conditions.
• Hough Transform: The Hough Transform is a technique for detecting lines and shapes in an image. The Hough Transform works by converting points in an image to a parameter space, where lines and shapes can be detected by looking for clusters of points that correspond to the same line or shape. The Hough Transform can be useful for detecting objects that have a specific shape or structure, such as circles or rectangles.