Yang Yang, Song Pinde, Wang Yongchao, Cao Lijia
School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China.
School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2024 Dec 2;24(23):7711. doi: 10.3390/s24237711.
Lightweight object detection algorithms play a paramount role in unmanned aerial vehicles (UAVs) remote sensing. However, UAV remote sensing requires target detection algorithms to have higher inference speeds and greater accuracy in detection. At present, most lightweight object detection algorithms have achieved fast inference speed, but their detection precision is not satisfactory. Consequently, this paper presents a refined iteration of the lightweight object detection algorithm to address the above issues. The MobileNetV3 based on the efficient channel attention (ECA) module is used as the backbone network of the model. In addition, the focal and efficient intersection over union (FocalEIoU) is used to improve the regression performance of the algorithm and reduce the false-negative rate. Furthermore, the entire model is pruned using the convolution kernel pruning method. After pruning, model parameters and floating-point operations (FLOPs) on VisDrone and DIOR datasets are reduced to 1.2 M and 1.5 M and 6.2 G and 6.5 G, respectively. The pruned model achieves 49 frames per second (FPS) and 44 FPS inference speeds on Jetson AGX Xavier for VisDrone and DIOR datasets, respectively. To fully exploit the performance of the pruned model, a plug-and-play structural re-parameterization fine-tuning method is proposed. The experimental results show that this fine-tuned method improves mAP@0.5 and mAP@0.5:0.95 by 0.4% on the VisDrone dataset and increases mAP@0.5:0.95 by 0.5% on the DIOR dataset. The proposed algorithm outperforms other mainstream lightweight object detection algorithms (except for FLOPs higher than SSDLite and mAP@0.5 Below YOLOv7 Tiny) in terms of parameters, FLOPs, mAP@0.5, and mAP@0.5:0.95. Furthermore, practical validation tests have also demonstrated that the proposed algorithm significantly reduces instances of missed detection and duplicate detection.
轻量级目标检测算法在无人机遥感中起着至关重要的作用。然而，无人机遥感要求目标检测算法具有更高的推理速度和更高的检测精度。目前，大多数轻量级目标检测算法已经实现了快速的推理速度，但其检测精度并不令人满意。因此，本文提出了一种轻量级目标检测算法的优化迭代，以解决上述问题。基于高效通道注意力（ECA）模块的MobileNetV3被用作模型的骨干网络。此外，焦点与高效交并比（FocalEIoU）被用于提高算法的回归性能并降低漏检率。此外，使用卷积核剪枝方法对整个模型进行剪枝。剪枝后，VisDrone和DIOR数据集上的模型参数和浮点运算（FLOPs）分别减少到1.2M和1.5M以及6.2G和6.5G。剪枝后的模型在Jetson AGX Xavier上针对VisDrone和DIOR数据集分别实现了每秒49帧（FPS）和44 FPS的推理速度。为了充分发挥剪枝后模型的性能，提出了一种即插即用的结构重参数化微调方法。实验结果表明，这种微调方法在VisDrone数据集上使mAP@0.5和mAP@0.5:0.95提高了0.4%，在DIOR数据集上使mAP@0.5:0.95提高了0.5%。所提出的算法在参数、FLOPs、mAP@0.5和mAP@0.5:0.95方面优于其他主流轻量级目标检测算法（除了FLOPs高于SSDLite且mAP@0.5低于YOLOv7 Tiny）。此外，实际验证测试也表明，所提出的算法显著减少了漏检和重复检测的情况。
