creating a vision system for apple harvesting robot using YOLO algorithm
steps to develop a custom detector
1.Create yolov4 and training folders in your google drive
received gpu
2.Mount drive, link your folder and navigate to the yolov4 folder
3.Clone the Darknet git repository
4.Create & upload the files we need for training ( i.e. “obj.zip” , “yolov4- custom.cfg”, “obj.data”, “obj.names” and “process.py” ) to your drive
Data set construction
Single object with no occlusion,
Multiple objects with occlusion,
Clusters of apples,
Illumination variation,
Shading conditions,
Multiple objects with or without occlusion
5.Make changes in the Makefile to enable OPENCV and GPU
6.Run make command to build darknet
7.Copy the files “obj.zip”, “yolov4-custom.cfg”, “obj.data”, “obj.names”, and “process.py” from the yolov4 folder to the darknet directory
8.Run the process.py python script to create the train.txt & test.txt files
9.Download the pre-trained YOLOv4 weights
10.Train the detector
11.Check performance
performence graph
performence check by mean average precision(mAP)
12.Test your custom Object Detector
Test results on images
Test results on webcam images
Testing on a higher illumination condtion
Testing on a highershading condtion
Test results on videos
medium1.mp4medium2.mp4medium3.mp4
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creating a vision system for apple harvesting robot using YOLO algorithm