Ananthakrishnan Balasundaram1,*, Anshuman Mohanty2, Ayesha Shaik1, Krishnadoss Pradeep2, Kedalu Poornachary Vijayakumar2, Muthu Subash Kavitha3
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2751-2769, 2023, DOI:10.32604/cmc.2023.044374
Abstract Automated object detection has received the most attention over the years. Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions. State-of-the-art object detectors tend to fare well in object detection during daytime conditions. However, their performance is severely hampered in night light conditions due to poor illumination. To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions. Firstly, the preprocessing strategies involve using the Zero-DCE++ approach to enhance lowlight images. It is followed by optimizing the existing YOLOv5… More >
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