基于重参数化的轻量化非机动车目标检测. (Chinese)

Lightweight Non-Motor Vehicle Target Detection Based on Re-Parameterization. (English)

Aiming at the problems that the number of targets in non-motor vehicle detection is easy to be occluded, the number of detection model parameters is large and the detection speed is slow, combining YOLOv8 algorithm and HGNetV2 network, a lightweight non-motor vehicle target detection algorithm based on re-parameterization is proposed, called RCH-YOLO. Firstly, the improved re-parameterized backbone network based on HGNetV2 is used to reduce the number of parameters and calculations and improve the detection speed. Sceondly, Slim-neck is used to improve the neck network, and TGConv convolution is used in the detector to reduce the complexity of the network while maintaining accuracy. Finally, the Wise-IoU loss function is used to calculate the positioning loss, accelerate the convergence speed of the model, and improve the recognition accuracy of overlapping targets. Experimental results show that compared with the baseline model, the average detection accuracy of the proposed algorithm is improved by 0.3 percentage points, and the number of parameters, model weight and GFLOPs are reduced by 47.9%, 44.2% and 54.9% respectively, which verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

针对非机动车检测中目标数量多易被遮挡、检测模型参数量较大且检测速度慢的问题, 结合 YOLOv8 算法 和 HGNetV2 网络,提出一种基于重参数化的轻量化非机动车目标检测算法 RCH-YOLO。使用基于 HGNetV2 改进的 重参数化骨干网络, 减少参数量和计算量, 提高检测速度;在此基础上, 使用 Slim-neck 改进颈部网络, 并在检测器中 使用 TGConv 卷积, 降低网络复杂度的同时保持准确性, 采用 Wise-IoU 损失函数计算定位损失, 加速模型收敛速度, 提高对重叠目标的识别准确率。实验结果显示, 相较于基准模型, RCH-YOLO 算法平均检测精度提高了 0.3 个百分 点, 参数量、模型大小和 GFLOPs 分别降低了 47.9%、44.2%和 54.9%, 验证了其有效性。 [ABSTRACT FROM AUTHOR]

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