A Noble Approach to the Object Detection in Defense System in Terms of Image Processing

Object identification is very important for modern defense systems because it helps ensure security and extends support in various operations. Realistic images come with issues of low contrast, motion blur, and noise, which ultimately affect detection performance. This research seeks to come up with ways of enhancing a procedure that enhances image quality and consequently detection. The method used here applies Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast. Then, Gaussian Blur & Median Blur are used to mimic and reduce the motion blur effect and eliminate noise respectively. Finally, sharpening is done to highlight edges. As a result, the image quality has significantly improved. Sharper edges, improved contrast and less noise will help in better AI detection. In the future, we plan to add machine learning models to support real-time detection which will make the system adaptable to complex environments.