基于改进YOLOv4算法的轻量化网络设计与实现

在嵌入式设备上进行目标检测时易受能耗和功耗等限制，使得传统目标检测算法效果不佳。为此，对YOLOv4算法进行优化，设计YOLOv4-Mini网络结构，将其特征提取网络由CSPDarkNet53改为MobileNetv3-large并进行INT8量化处理，其中网络结构利用PW和DW卷积操作代替传统卷积操作以大幅减少计算量。采用SE模块为通道施加注意力机制，激活函数层运用h-swish非线性激活函数，在保证精度的情况下降低网络计算量。同时，通过量化感知训练将权重转为INT8类型，以实现模型轻量化，进一步降低网络参数量和计算量，从而在嵌入式设备上完成无人机数据集的目标检测任务。在NVIDIA Jetson Xavier NX设备上进行测试，结果显示，YOLOv4-MobileNetv3网络的mAP为34.3%，FPS为30，YOLOv4-Mini网络的mAP为32.5%，FPS为73，表明YOLOv4-Mini网络能够在低功耗、低能耗的嵌入式设备上完成目标实时检测任务。

Design and Implementation of Lightweight Network Based on Improved YOLOv4 Algorithm

Target detection using embedded devices is limited by energy and power consumption,deteriorating the performance of traditional target detection algorithms. Therefore,to address this issue,the YOLOv4 algorithm is optimized;the YOLOv4-Mini network structure is designed;the feature extraction network is changed from CSPDarkNet53 to MobileNetv3-large;and INT8 quantization processing is carried out. The network structure uses PW and DW convolution operations to replace the traditional convolution operation to greatly reduce the amount of calculation. The SE module is used to apply attention mechanism to the channel,and h-swish nonlinear activation function is used in the activation function layer to reduce the amount of network calculation while ensuring accuracy.Concurrently,the weight is transformed into INT8 type through quantitative perception training to realize the lightweight of the model and further reduce the amount of network parameters and computation,in addition to completing the target detection task of UAV data set on embedded devices.The test results on NVIDIA Jetson Xavier NX show that the mAP of YOLOv4-MobileNetv3 network is 34.3%,the FPS is 30,the mAP of YOLOv4-Mini network is 32.5%,and the FPS is73 indicating that the YOLOv4-Mini network can complete the target real-time detection task on the embedded device with low power consumption and low energy consumption.