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Small object detection via UAV (Unmanned Aerial Vehicle) images captured fromdrones and radar is a complex task with several formidable challenges. Thisdomain encompasses numerous complexities that impede the accurate detection andlocalization of small objects. To address these challenges, we propose a novelmethod called JointYODNet for UAVs to detect small objects, leveraging a jointloss function specifically designed for this task. Our method revolves aroundthe development of a joint loss function tailored to enhance the detectionperformance of small objects. Through extensive experimentation on a diversedataset of UAV images captured under varying environmental conditions, weevaluated different variations of the loss function and determined the mosteffective formulation. The results demonstrate that our proposed joint lossfunction outperforms existing methods in accurately localizing small objects.Specifically, our method achieves a recall of 0.971, and a F1Score of 0.975,surpassing state-of-the-art techniques. Additionally, our method achieves amAP@.5(%) of 98.6, indicating its robustness in detecting small objects acrossvarying scales

Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above 100fps