UAVOD-Net: A Lightweight Yolov11n-Bob Cat Optimization Framework for High Precision Object Detection in Aerial Imagery

The rapid deployment of Unmanned Aerial Vehicles (UAVs) for tasks such as traffic monitoring, agricultural inspection, and public safety has significantly increased the volume of aerial visual data, yet efficient, real-time object detection remains a critical challenge, especially in complex urban environments. Existing studies show that object detection models often struggle with occlusion, small object sizes, and varying lighting conditions prevalent in drone-captured imagery, leading to sub-optimal performance and higher false detection rates. To address these limitations, this work proposes an enhanced UAV Object Detection (UAVOD-Net) framework leveraging the VisDrone dataset. Robust image preprocessing techniques are applied to improve contrast, remove noise, and standardize input dimensions, enhancing feature clarity. For detection, the You Only Look at Once with Version-11 based Nano variant (YOLOv11n) model, known for its lightweight architecture and high-speed processing, is deployed to efficiently localize objects such as pedestrians, vehicles, and bicycles. Further performance improvement is achieved through the integration of the Bob Cat Optimization (BCO) algorithm, a nature-inspired metaheuristic approach designed to optimize network weights and hyperparameters. BCO enhances convergence speed, reduces model loss, and improves detection accuracy under challenging conditions like occlusions and variable scales. This combined methodology significantly boosts object detection precision and reliability for UAV-based surveillance and monitoring applications. The proposed UAVOD-Net achieved an overall Precision of 0.594, Recall of 0.485, mAP@50 of 0.516, and mAP@50–95 of 0.326 for all object classes. These results demonstrate improved detection accuracy and robustness for UAV-based aerial imagery. Furthermore, the same UAVOD-Net applied on Detection in Adverse Weather Nature (DAWN) dataset, where proposed UAVOD-Net resulted in superior object detection and classification performance in presence of adverse weather conditions compared to existing approaches.