工业级别的目标检测关注的不仅仅是精度，还有速度，能达到实时是最理想状态，一般来讲，目标检测实时大于12.5fps被认为是实时，针对TX2利用yolov4检测博主做了一个详细的调研和测试。
1.下载darknet,网址如下：
git clone https://github.com/AlexeyAB/darknet.git
2.配置makefile文件
由于TX2已经刷机Jetpack4.4,TX2里面有gpu,cuda和cudnn等，修改makefile文件如下：
GPU=1
CUDNN=1
OPENCV=1
3.在darknet路径下编译如下
make4.下载权重，放到darknet目录下
# yolov4-tiny.weights
wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights
# yolov4.weights
wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4.weights
5.测试（yolov4 and yolov4-tiny）
(1).测试图片
./darknet detector test cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights data/dog.jpg
./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/dog.jpg (2).测试视频
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights data/sample.mp4
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights data/sample.mp4
(3).实时测试板载摄像头 (CSI摄像头实时检测)
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=30/1 ! nvvidconv flip-method=0 ! video/x-raw, width=1280, height=720, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink"
./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1280, height=720, format=NV12, framerate=30/1 ! nvvidconv flip-method=0 ! video/x-raw, width=1280, height=720, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink"(4).实时检测usb摄像头
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights -c 1
./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 1
(5).rstp实时检测
./darknet detector demo cfg/coco.data cfg/yolov4-tiny.cfg yolov4-tiny.weights rstp://admin:admin@20.10.7.34/0
./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights rstp://admin:admin@20.10.7.34/06 总结
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下集预告：如何按照自己的需求训练模型以及二次开发
版权声明：本文为CSDN博主「fengfeng，Z」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_38522972/article/details/111876156
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