Apple detection during different growth stages in orchards using the improved YOLO-V3 model
•Proposing an improved YOLO-V3 network processed by DenseNet method.•Realizing the detection of apples in three different growth stages in orchards.•Realizing real-time detection of apples in high resolution images.•Realizing the detection of apples under occlusion and overlap conditions. Real-time...
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| Published in | Computers and electronics in agriculture Vol. 157; pp. 417 - 426 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.02.2019
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
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| Abstract | •Proposing an improved YOLO-V3 network processed by DenseNet method.•Realizing the detection of apples in three different growth stages in orchards.•Realizing real-time detection of apples in high resolution images.•Realizing the detection of apples under occlusion and overlap conditions.
Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304s per frame at 3000 × 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards. |
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| AbstractList | Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304 s per frame at 3000 x 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards. •Proposing an improved YOLO-V3 network processed by DenseNet method.•Realizing the detection of apples in three different growth stages in orchards.•Realizing real-time detection of apples in high resolution images.•Realizing the detection of apples under occlusion and overlap conditions. Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304s per frame at 3000 × 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards. Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304s per frame at 3000 × 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards. |
| Author | Wang, Zhe Yang, Guodong Liang, Zize Tian, Yunong Li, En Wang, Hao |
| Author_xml | – sequence: 1 givenname: Yunong surname: Tian fullname: Tian, Yunong – sequence: 2 givenname: Guodong surname: Yang fullname: Yang, Guodong – sequence: 3 givenname: Zhe orcidid: 0000-0001-8766-9126 surname: Wang fullname: Wang, Zhe – sequence: 4 givenname: Hao surname: Wang fullname: Wang, Hao – sequence: 5 givenname: En orcidid: 0000-0002-4412-2953 surname: Li fullname: Li, En email: en.li@ia.ac.cn – sequence: 6 givenname: Zize surname: Liang fullname: Liang, Zize |
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| Snippet | •Proposing an improved YOLO-V3 network processed by DenseNet method.•Realizing the detection of apples in three different growth stages in orchards.•Realizing... Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour,... |
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| SubjectTerms | Apple images acquisition Apples branches Color data collection Deep learning developmental stages fruits Image augmentation leaves lighting Model testing Occlusion Orchards Real time Real-time detection Training Transformations Variations YOLOV3-dense |
| Title | Apple detection during different growth stages in orchards using the improved YOLO-V3 model |
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