Abstract
As drone-based object detection technology continues to evolve, the demand is shifting from merely detecting objects to enabling users to accurately identify specific targets. For example, users can input particular targets as prompts to precisely detect desired objects. To address this need, an efficient text-guided object detection model has been developed to enhance the detection of small objects. Specifically, an improved version of the existing YOLO-World model is introduced. The proposed method replaces the C2f layer in the YOLOv8 backbone with a C3k2 layer, enabling more precise representation of local features, particularly for small objects or those with clearly defined boundaries. Additionally, the proposed architecture improves processing speed and efficiency through parallel processing optimization, while also contributing to a more lightweight model design. Comparative experiments on the VisDrone dataset show that the proposed model outperforms the original YOLO-World model, with precision increasing from 40.6% to 41.6%, recall from 30.8% to 31%, F1 score from 35% to 35.5%, and mAP@0.5 from 30.4% to 30.7%, confirming its enhanced accuracy. Furthermore, the model demonstrates superior lightweight performance, with the parameter count reduced from 4 million to 3.8 million and FLOPs decreasing from 15.7 billion to 15.2 billion. These results indicate that the proposed approach provides a practical and effective solution for precise object detection in drone-based applications.
Abstract (translated)
随着基于无人机的目标检测技术的不断进步，用户的需求已从单纯的物体识别转向了能够精准定位特定目标的方向。例如，用户可以通过输入具体的提示词来精确地探测到想要找的对象。为了满足这一需求，开发了一种高效的文本引导式目标检测模型，以提高对小目标的检测效果。具体来说，这是现有YOLO-World模型的一个改进版本。 所提出的方法在YOLOv8骨干网络中用C3k2层替换了原有的C2f层，这使得局部特征的表示更加精确，特别是在处理较小物体或边界清晰的目标时表现尤为突出。此外，该架构通过优化并行处理提高了处理速度和效率，并且有助于构建更为轻量化的模型设计。 在VisDrone数据集上的比较实验表明，所提出的模型优于原始的YOLO-World模型，在精度（从40.6%提升至41.6%）、召回率（从30.8%增加到31%）、F1分数（从35%提高到35.5%）以及mAP@0.5指标上均有改善，这证实了其在精确度上的显著提升。此外，该模型还展示了出色的轻量化性能，在参数数量上减少了20万至380万个，并将计算量（FLOPs）从157亿次减少到了152亿次。 这些结果显示所提出的方法为基于无人机应用的精准目标检测提供了一个实用且有效的解决方案。
URL
https://arxiv.org/abs/2602.19503
