An Efficient Method for Object Detection with Automatic Illumination Correction

Recent advancements in artificial intelligence and computer vision have led to the automation of many conventional surveillance techniques, especially in smart city applications. However, object detection systems often struggle in poorly lit environments and may suffer from slow processing speeds. To address these restrictions, this paper proposes an adaptive illumination correction technique using an enhanced Logarithmic Image Processing model. The method improves object visibility in low-light video frames and enhances detection performance. Additionally, a customized deep convolutional neural network is developed to accurately detect objects after applying the illumination correction. The combined framework is evaluated on standard datasets and demonstrates superior robustness to varying illumination conditions compared to existing state-of-the-art methods. The results show significant improvements in metrics such as accuracy, recall, precision, and F-measure, proving the effectiveness of the proposed approach.