Improved Field Obstacle Detection Algorithm Based on YOLOv8

To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast dete...

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Veröffentlicht in:Agriculture (Basel) Jg. 14; H. 12; S. 2263
Hauptverfasser: Zhou, Xinying, Chen, Wenming, Wei, Xinhua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2024
Schlagworte:
Accuracy
Agricultural equipment
Agricultural land
Agricultural technology
Agricultural vehicles
Agriculture
Algorithms
BiFPN
Boxes
CBAM
Computer vision
Datasets
Deep learning
Detectors
Electric power
Farm machinery
Farmers
field obstacle detection
Fieldwork
Frames per second
Harvesters
Image quality
Lighting
Obstacle avoidance
Pylons
Real time
Sensors
Target detection
Task complexity
Tractors
Vision systems
WIoU v3
YOLOv8
ISSN:2077-0472, 2077-0472
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Abstract To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast detection and recognition of obstacles such as people, tractors, and electric power pylons in the field. This detection model was built upon the YOLOv8 architecture with three main improvements. First, to adapt to different tasks and complex environments in the field, improve the sensitivity of the detector to various target sizes and positions, and enhance detection accuracy, the CBAM (Convolutional Block Attention Module) was integrated into the backbone layer of the benchmark model. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture took the place of the original PANet to enhance the fusion of features across multiple scales, thereby increasing the model’s capacity to distinguish between the background and obstacles. Third, WIoU v3 (Wise Intersection over Union v3) optimized the target boundary loss function, assigning greater focus to medium-quality anchor boxes and enhancing the detector’s overall performance. A dataset comprising 5963 images of people, electric power pylons, telegraph poles, tractors, and harvesters in a farmland environment was constructed. The training set comprised 4771 images, while the validation and test sets each consisted of 596 images. The results from the experiments indicated that the enhanced model attained precision, recall, and average precision scores of 85.5%, 75.1%, and 82.5%, respectively, on the custom dataset. This reflected increases of 1.3, 1.2, and 1.9 percentage points when compared to the baseline YOLOv8 model. Furthermore, the model reached 52 detection frames per second, thereby significantly enhancing the detection performance for common obstacles in the field. The model enhanced by the previously mentioned techniques guarantees a high level of detection accuracy while meeting the criteria for real-time obstacle identification in unmanned agricultural equipment during fieldwork.
AbstractList To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast detection and recognition of obstacles such as people, tractors, and electric power pylons in the field. This detection model was built upon the YOLOv8 architecture with three main improvements. First, to adapt to different tasks and complex environments in the field, improve the sensitivity of the detector to various target sizes and positions, and enhance detection accuracy, the CBAM (Convolutional Block Attention Module) was integrated into the backbone layer of the benchmark model. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture took the place of the original PANet to enhance the fusion of features across multiple scales, thereby increasing the model’s capacity to distinguish between the background and obstacles. Third, WIoU v3 (Wise Intersection over Union v3) optimized the target boundary loss function, assigning greater focus to medium-quality anchor boxes and enhancing the detector’s overall performance. A dataset comprising 5963 images of people, electric power pylons, telegraph poles, tractors, and harvesters in a farmland environment was constructed. The training set comprised 4771 images, while the validation and test sets each consisted of 596 images. The results from the experiments indicated that the enhanced model attained precision, recall, and average precision scores of 85.5%, 75.1%, and 82.5%, respectively, on the custom dataset. This reflected increases of 1.3, 1.2, and 1.9 percentage points when compared to the baseline YOLOv8 model. Furthermore, the model reached 52 detection frames per second, thereby significantly enhancing the detection performance for common obstacles in the field. The model enhanced by the previously mentioned techniques guarantees a high level of detection accuracy while meeting the criteria for real-time obstacle identification in unmanned agricultural equipment during fieldwork.
Audience Academic
Author Zhou, Xinying
Wei, Xinhua
Chen, Wenming
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Cites_doi 10.3390/agriculture14101807
10.1109/CVPR.2016.91
10.1016/j.ijleo.2021.167754
10.1109/ICCV.2015.169
10.1109/JPROC.2023.3238524
10.1007/978-3-030-01234-2_1
10.3390/rs13061064
10.1109/ITSC.2018.8569311
10.1109/SMC53992.2023.10394415
10.3390/make5040083
10.3390/rs16203810
10.1109/CVPR.2018.00913
10.1016/j.patcog.2017.10.013
10.3390/agriculture14081420
10.1109/CVPR.2017.106
10.1371/journal.pone.0259283
10.3390/plants13172388
10.1007/978-3-319-46448-0_2
10.1109/CVPR.2014.81
10.1109/CVPR42600.2020.01079
10.1007/978-3-031-26409-2_27
10.1109/CVPR.2017.690
10.1016/j.neucom.2021.03.091
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References Terven (ref_23) 2023; 5
Niu (ref_24) 2021; 452
Zou (ref_3) 2023; 111
ref_14
ref_13
ref_12
ref_11
ref_10
ref_30
ref_19
ref_18
ref_17
ref_16
ref_15
ref_25
ref_22
ref_21
ref_1
Pathak (ref_20) 2021; 246
ref_2
ref_29
ref_28
ref_27
ref_26
ref_9
ref_8
Gu (ref_5) 2018; 77
ref_4
ref_7
ref_6
References_xml – ident: ref_7
– ident: ref_10
  doi: 10.3390/agriculture14101807
– ident: ref_9
– ident: ref_17
  doi: 10.1109/CVPR.2016.91
– ident: ref_30
– volume: 246
  start-page: 167754
  year: 2021
  ident: ref_20
  article-title: Content-based image retrieval using feature-fusion of GroupNormalized-Inception-Darknet-53 features and handcraft features
  publication-title: Optik
  doi: 10.1016/j.ijleo.2021.167754
– ident: ref_6
  doi: 10.1109/ICCV.2015.169
– volume: 111
  start-page: 257
  year: 2023
  ident: ref_3
  article-title: Object detection in 20 years: A survey
  publication-title: Proc. IEEE
  doi: 10.1109/JPROC.2023.3238524
– ident: ref_28
  doi: 10.1007/978-3-030-01234-2_1
– ident: ref_1
  doi: 10.3390/rs13061064
– ident: ref_2
  doi: 10.1109/ITSC.2018.8569311
– ident: ref_12
  doi: 10.1109/SMC53992.2023.10394415
– volume: 5
  start-page: 1680
  year: 2023
  ident: ref_23
  article-title: A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas
  publication-title: Mach. Learn. Knowl. Extr.
  doi: 10.3390/make5040083
– ident: ref_21
– ident: ref_13
  doi: 10.3390/rs16203810
– ident: ref_27
  doi: 10.1109/CVPR.2018.00913
– volume: 77
  start-page: 354
  year: 2018
  ident: ref_5
  article-title: Recent advances in convolutional neural networks
  publication-title: Pattern Recognit.
  doi: 10.1016/j.patcog.2017.10.013
– ident: ref_16
  doi: 10.3390/agriculture14081420
– ident: ref_25
– ident: ref_26
  doi: 10.1109/CVPR.2017.106
– ident: ref_22
  doi: 10.1371/journal.pone.0259283
– ident: ref_14
  doi: 10.3390/plants13172388
– ident: ref_15
– ident: ref_8
  doi: 10.1007/978-3-319-46448-0_2
– ident: ref_4
  doi: 10.1109/CVPR.2014.81
– ident: ref_19
– ident: ref_29
  doi: 10.1109/CVPR42600.2020.01079
– ident: ref_11
  doi: 10.1007/978-3-031-26409-2_27
– ident: ref_18
  doi: 10.1109/CVPR.2017.690
– volume: 452
  start-page: 48
  year: 2021
  ident: ref_24
  article-title: A review on the attention mechanism of deep learning
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2021.03.091
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SubjectTerms Accuracy
Agricultural equipment
Agricultural land
Agricultural technology
Agricultural vehicles
Agriculture
Algorithms
BiFPN
Boxes
CBAM
Computer vision
Datasets
Deep learning
Detectors
Electric power
Farm machinery
Farmers
field obstacle detection
Fieldwork
Frames per second
Harvesters
Image quality
Lighting
Obstacle avoidance
Pylons
Real time
Sensors
Target detection
Task complexity
Tractors
Vision systems
WIoU v3
YOLOv8
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