Jiechi Zhang

Abstract Low-light object detection frequently suffers from weak contrast, high noise, and poor performance on small objects. Existing methods often adopt image enhancement to improve detection performance, which results in substantial computational resource consumption. To address these challenges, we propose TinyDark-YOLO, an adaptive and efficient low-light object detection algorithm based on YOLO11.

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