工業級別的目標檢測關注的不僅僅是精度，還有速度，能達到實時是最理想狀態，一般來講，目標檢測實時大于12.5fps被認為是實時，針對TX2利用yolov4檢測博主做了一個詳細的調研和測驗，
1.下載darknet,網址如下：
git clone https://github.com/AlexeyAB/darknet.git
2.配置makefile檔案
由于TX2已經刷機Jetpack4.4,TX2里面有gpu,cuda和cudnn等，修改makefile檔案如下：
GPU=1
CUDNN=1
OPENCV=1
3.在darknet路徑下編譯如下
make4.下載權重，放到darknet目錄下
# 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.測驗（yolov4 and yolov4-tiny）
(1).測驗圖片
./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).測驗視頻
./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).實時測驗板載攝像頭 (CSI攝像頭實時檢測)
./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).實時檢測usb攝像頭
./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實時檢測
./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 總結
評論區留言哦
下集預告：如何按照自己的需求訓練模型以及二次開發
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