摘要
文章基于YOLOv4进行了蛇类检测。在谷歌的Open Image数据集中下载已标注蛇类图片,使用谷歌的Colab平台进行实验,在Darknet框架下对网络模型进行训练。经对比,YOLOv4的最终性能高于常见的onestage检测算法,相近准确度下速度快于twostage检测算法。最终结果显示,YOLOv4在识别蛇类图像时准确度达95.55%,平均检测时间为37 ms,帧处理速率达27FPS(帧/秒)。该检测速度和检测精度满足大部分背景下蛇类检测的需求,使蛇类检测与识别具备了可行性。
In this paper,snake detection is carried out based on YOLOv4.Download the marked snake pictures in Google,s Open Image dataset,conduct experiments using Google,s Colab platform,and train the network model under the Darknet framework.After comparison,the final performance of YOLOv4 is higher than the common onestage detection algorithm,and the speed of it is faster than the twotage detection algorithm in the context of similar accuracy.The final results show that the accuracy of YOLOv4 in identifying snake images reaches 95.55%,the average detection time is 37 ms,and the frame processing rate reaches 27 FPS(frames/second).The detection speed and accuracy meet the needs of snake detection in most backgrounds,which makes snake detection and recognition feasible.
作者
王博鑫
李丹
WANG Boxin;LI Dan(School of Computer and Software,Jincheng College of Sichuan University,Chengdu 611731,China)
出处
《现代信息科技》
2021年第13期34-36,共3页
Modern Information Technology
