Automatic detection of oil palm fruits from UAV images using an improved YOLO model
Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative solution for precision agriculture which requires accurate visual information of the targets. Current state-of-the-art one-stage object detectio...
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| Published in | The Visual computer Vol. 38; no. 7; pp. 2341 - 2355 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.07.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0178-2789 1432-2315 |
| DOI | 10.1007/s00371-021-02116-3 |
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| Abstract | Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative solution for precision agriculture which requires accurate visual information of the targets. Current state-of-the-art one-stage object detection method provides excellent detection accuracy; however, it is computationally intensive and impractical for embedded system. This paper proposed an improved YOLO model to detect oil palm loose fruits from unmanned aerial vehicle images. In order to improve the robustness of the detection system, the images are augmented by brightness, rotation, and blurring to simulate the actual natural environment. The proposed improved YOLO model adopted several improvements; densely connected neural network for better feature reuse, swish activation function, multi-layer detection to enhance detection on small targets and prior box optimization to obtain accurate bounding box information. The experimental results show that the proposed model achieves outstanding average precision of 99.76% with detection time of 34.06 ms. In addition, the proposed model is also light in weight size and requires less training time which is significant in reducing the hardware costs. The results exhibit the superiority of the proposed improved YOLO model over several existing state-of-the-art detection models. |
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| AbstractList | Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative solution for precision agriculture which requires accurate visual information of the targets. Current state-of-the-art one-stage object detection method provides excellent detection accuracy; however, it is computationally intensive and impractical for embedded system. This paper proposed an improved YOLO model to detect oil palm loose fruits from unmanned aerial vehicle images. In order to improve the robustness of the detection system, the images are augmented by brightness, rotation, and blurring to simulate the actual natural environment. The proposed improved YOLO model adopted several improvements; densely connected neural network for better feature reuse, swish activation function, multi-layer detection to enhance detection on small targets and prior box optimization to obtain accurate bounding box information. The experimental results show that the proposed model achieves outstanding average precision of 99.76% with detection time of 34.06 ms. In addition, the proposed model is also light in weight size and requires less training time which is significant in reducing the hardware costs. The results exhibit the superiority of the proposed improved YOLO model over several existing state-of-the-art detection models. Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative solution for precision agriculture which requires accurate visual information of the targets. Current state-of-the-art one-stage object detection method provides excellent detection accuracy; however, it is computationally intensive and impractical for embedded system. This paper proposed an improved YOLO model to detect oil palm loose fruits from unmanned aerial vehicle images. In order to improve the robustness of the detection system, the images are augmented by brightness, rotation, and blurring to simulate the actual natural environment. The proposed improved YOLO model adopted several improvements; densely connected neural network for better feature reuse, swish activation function, multi-layer detection to enhance detection on small targets and prior box optimization to obtain accurate bounding box information. The experimental results show that the proposed model achieves outstanding average precision of 99.76% with detection time of 34.06 ms. In addition, the proposed model is also light in weight size and requires less training time which is significant in reducing the hardware costs. The results exhibit the superiority of the proposed improved YOLO model over several existing state-of-the-art detection models. |
| Author | Junos, Mohamad Haniff Thannirmalai, Subbiah Dahari, Mahidzal Mohd Khairuddin, Anis Salwa |
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| Snippet | Manual harvesting of loose fruits in the oil palm plantation is both time consuming and physically laborious. Automatic harvesting system is an alternative... |
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| SubjectTerms | Accuracy Artificial Intelligence Blurring Cameras Classification Computer Graphics Computer Science Datasets Deep learning Embedded systems Fruits Harvesting Image Processing and Computer Vision Methods Multilayers Neural networks Object recognition Original Article Target detection Unmanned aerial vehicles |
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| Title | Automatic detection of oil palm fruits from UAV images using an improved YOLO model |
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