LCEA-YOLO: Improving Small Object Detection in Aerial Imagery Using Local Contrast Enhancement Attention and Inner-CIoU Loss
Corresponding author: Sara Ennaama
Abstract
This study proposed the Local Contrast Enhancement Attention - You Only Look Once (LCEA-YOLO) model to address the challenge of detecting small objects in high-altitude Unmanned Aerial Vehicle (UAV) images. LCEA-YOLO is a customized detector developed using the YOLOv10n architecture that embeds an LCEA module to emphasize local subtle contrast variations, as well as an Inner-CIoU loss function that enhances the regression ability for smaller objects. Experiments on the UAVDT dataset were conducted, with the results demonstrating that LCEA-YOLO exceeded other baseline algorithms with an overall mAP@0.5 of 47.2% and mAP@0.5:0.95 of 29.9%. Additionally, the model presented remarkable resilience on challenging under-represented categories, yielding improvements of 15.0% and 7.6% in detection accuracy for trucks and buses, respectively. These findings validated the benefit of targeted local contrast enhancement and scale-aware regression for real-time UAV detection.
Keywords:
object detection, Unmanned Aerial Vehicles (UAV), YOLOv10, attention mechanism, Inner-CIoU loss, small object detection, deep learningDownloads
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Copyright (c) 2026 Sara Ennaama, Hassan Silkan, Abderrahim Tahiri, Faouzia Ennaama
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