一种改进CenterNet的轻量化目标检测算法

Lightweight object detection algorithm based on the improved CenterNet

CenterNet算法结构复杂,导致其参数量大、计算复杂度高和检测速度较慢。针对这一问题,提出了一种CenterNet-encoder算法。该算法使用深度为104层的沙漏网络作为backbone,并将其中的残差模块替换为fire模块来减少算法的参数量,提高算法的计算速度;另外,在backbone和head之间加入了编码层,在不损失分辨率的同时增大了感受野,减少了内存的占用,让输出囊括更多尺度的信息;最后,使用均方误差损失进行边界框的回归,加快算法的收敛,进一步提升了算法的检测精度。CenterNet-encoder算法最终在MS-COCO test-dev数据集上的平均检测精度为40.5%,参数量为47×10^(6)。在AMD5900X/32GB/RTX3090环境配置下,检测速度达到了18帧/s。实验结果表明,CenterNet-encoder算法虽然牺牲了一定的精度,但参数量比原算法下降了约77.6%,同时检测速度提升了约69.3%。与其他轻量化目标检测算法相比,在参数量、推理时间和检测精度上也有一定的优势。

Due to the complex structure,there are a large number of parameters in the CenterNet,which leads to a high computational complexity and long inference time.To solve this problem,a CenterNet-encoder algorithm is proposed for lightweight object detection.First,the fire module is used in the backbone to reduce the number of parameters and increase the calculation speed.Then,an encoding layer is utilized between the backbone and the head,which can increase the receptive field and obtain more accurate corners and center points for heatmaps.Finally,MSE loss is employed in bounding box regression,which accelerates the convergence and further improves the performance.The proposed algorithm achieves 40.5 AP on the MS-COCO test-dev benchmark with 47M parameters.Under the AMD5900X\\32GB\\RTX3090 environment configuration,the detection speed reaches 18FPS.Experimental results show that the performance of the proposed method is better than other lightweight methods in the number of parameters,inference time and detection accuracy.Although the precision of the proposed method is slightly lower than that of the CenterNet,the number of parameters is reduced by 77.6%,and the inference speed is increased by 69.3%.