Apple Detection in Natural Environment Using Deep Learning Algorithms
| Autor: | Yongxian Wang, Guantao Xuan, Jingrun Zhong, Yuanyuan Shao, Meng Zhang, Chong Gao, Qingguo Li, Hongxing Peng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION image process faster RCNN 02 engineering and technology 020901 industrial engineering & automation General Materials Science Pixel business.industry Deep learning apple detection General Engineering Cognitive neuroscience of visual object recognition 04 agricultural and veterinary sciences YOLOv3 Object detection 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering Transfer of learning business Algorithm lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 216772-216780 (2020) |
| ISSN: | 2169-3536 |
| Popis: | It is a challenging problem to detect the apple in natural environment using traditional object recognition algorithms due to occlusion, fluctuating illumination and complex backgrounds. Deep learning methods for object detection make impressive progress, which can automatically extract the number, pixel position, size and other features of apples from the images. In this paper, four deep learning recognition models, Faster RCNN based on AlexNet, Faster RCNN based on ResNet101, YOLOv3 based on DarkNet53 and improved YOLOv3 were employed to carry out recognition experiments on red and green apple under three illumination and two image sharpness conditions, with the transfer learning to accelerate the training process. The results showed that improved YOLOv3 model had the best recognition effect among the four detection models. F1 value of red apple recognition was 95.0%, 94.6% and 94.1% for normal, insufficient and excessive illumination, respectively, and F1 value of green apple recognition was 94.9%, 94.0% and 91.1%. There were F1 value of 92.8% and 92.1% for red and green apple recognition in blurred images, respectively. Moreover, improved YOLOv3 algorithm still had the better performance for occlusion, spot, overlap and incomplete apples, with a recognition recall rate higher than 88.5%. It can be concluded that improved YOLOv3 algorithm can provide a more efficient way for apple detection in natural environment. |
| Databáze: | OpenAIRE |
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