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+--------------------------------+ 基于 ncnn + yolofast 的目标检测 +--------------------------------+ Yolo-Fastest: https://github.com/dog-qiuqiu/Yolo-Fastest yolo 神经网络的目标检测和分类,并且提供了可用于 ncnn 的模型文件 因此我们可以非常方便的移植到 ncnn 上 本程序基于腾讯的 ncnn + yolofast 模型,实现了目标检测和分类功能 可以识别出 82 种目标在图像中的位置,以及目标的分类(包括了人脸、人体、猫猫狗狗、汽车、各种动物...) 在 ubuntu、msys2 和 msc33x 嵌入式 linux 平台都可以编译通过和使用 (在 msc33x 平台实测,检测 312x168 的图像需要 370ms,还需要优化否则实用性不大) 编译和运行 ---------- 目前已经在三个平台上编译通过: 1. ubuntu - envsetup-for-ubuntu.sh 2. msys2 - envsetup-for-msys2.sh 3. msc33x - envsetup-for-msc33x.sh 编译时需要首先执行对应的 envsetup-xxx.sh 设定环境变量: source envsetup-for-msys2.sh 编译 libncnn 库: ./build-libncnn.sh 编译 src 下的源代码: cd src ./build.sh 最终生成 test 程序,可用于目标检测 ./test test.bmp yolo-fastest-1.1.param yolo-fastest-1.1.bin 第一个参数是要检测的图片文件 第二个参数是模型 param 文件路径 第二个参数是模型 bin 文件路径 rockcarry 2021-4-14
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