Authors - Chalani Dinitha, Saadh Jawwadh Abstract - Automated Image Enhancement from CCTV surveillance relies heavily on accurate image segmentation; however, real-world footage is often degraded by low illumination, motion blur, occlusion, and background clutter, causing conventional segmentation models to lose boundary precision and small object details. This paper proposes EdgeLite-CrimSegNet, a novel lightweight boundary-aware segmentation network designed specifically for crime scene analysis. Unlike existing fast segmentation models that prioritize global context, the proposed architecture adopts a boundary-first learning strategy, where crime-relevant contours are explicitly extracted and refined before region-level segmentation. A compact edge-aware encoder, boundary-guided feature refinement module, and progressive region filling strategy are introduced to improve segmentation accuracy while maintaining real-time performance. Experiments on CCTV frames derived from the UCF-Crime dataset demonstrate improved boundary preservation, higher IOU, and better segmentation of overlapping and small objects compared to conventional lightweight segmentation networks, confirming the suitability of EdgeLite-CrimSegNet for real-time surveillance applications.
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