Shen Peng, Wang Fulong, Luo Wei, Zhao Yongxiang, Li Lin, Zhang Guoqing, Zhu Yuchen
North China Institute of Aerospace Engineering, Langfang, 065000, China.
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Heliyon. 2024 Sep 24;10(19):e38316. doi: 10.1016/j.heliyon.2024.e38316. eCollection 2024 Oct 15.
In agriculture, specifically livestock monitoring, drones' ability to track multiple targets is essential for advancing the field. However, limited computing resources and unpredictable drone movements often cause issues like ambiguous video frames, object obstructions, and size deviations. These inconsistencies reduce tracking accuracy, making traditional algorithms inadequate for handling drone footage. This study introduces an enhanced deep learning-based multi-target drone tracker framework that enables real-time processing. The proposed method combines object detection and tracking by leveraging consecutive frame pairs to extract and share features, enhancing computational efficiency. It employs diverse loss functions to address class and sample distribution imbalances and includes a composite deblurring module to enhance detection accuracy. Object association utilizes a dual regress bounding box technique, aiding in object identification verification and predictive motion. Live tracking is achieved by predicting object locations in subsequent frames, enabling real-time tracking. Evaluation against leading benchmarks shows that the system improves precision and speed, achieving a 4.3 % increase in Multi-Object Tracking Accuracy (MOTA) and a 7.7 % boost in F1 score.
在农业领域，特别是牲畜监测方面，无人机追踪多个目标的能力对于推动该领域发展至关重要。然而，有限的计算资源和无人机运动的不可预测性常常导致诸如视频帧模糊、物体遮挡和尺寸偏差等问题。这些不一致性降低了跟踪精度，使得传统算法不足以处理无人机拍摄的 footage。本研究引入了一种基于深度学习的增强型多目标无人机跟踪器框架，该框架能够进行实时处理。所提出的方法通过利用连续帧对来提取和共享特征，将目标检测与跟踪相结合，提高了计算效率。它采用多种损失函数来解决类别和样本分布不均衡问题，并包括一个复合去模糊模块以提高检测精度。目标关联利用双回归边界框技术，有助于目标识别验证和预测运动。通过预测后续帧中的目标位置实现实时跟踪。与领先基准的评估表明，该系统提高了精度和速度，多目标跟踪精度（MOTA）提高了 4.3%，F1 分数提高了 7.7%。
