Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm
Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detec...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/4/999 |
| _version_ | 1797606786237202432 |
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| author | Liyuan Cai Jingming Liang Xing Xu Jieli Duan Zhou Yang |
| author_facet | Liyuan Cai Jingming Liang Xing Xu Jieli Duan Zhou Yang |
| author_sort | Liyuan Cai |
| collection | DOAJ |
| description | Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems. |
| first_indexed | 2024-03-11T05:20:01Z |
| format | Article |
| id | doaj.art-85a67bff716047f1a065a62886eade40 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T05:20:01Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-85a67bff716047f1a065a62886eade402023-11-17T17:56:07ZengMDPI AGAgronomy2073-43952023-03-0113499910.3390/agronomy13040999Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection AlgorithmLiyuan Cai0Jingming Liang1Xing Xu2Jieli Duan3Zhou Yang4College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaDetecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems.https://www.mdpi.com/2073-4395/13/4/999deep learningbanana pseudostemobject detectionYOLOV7 |
| spellingShingle | Liyuan Cai Jingming Liang Xing Xu Jieli Duan Zhou Yang Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm Agronomy deep learning banana pseudostem object detection YOLOV7 |
| title | Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm |
| title_full | Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm |
| title_fullStr | Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm |
| title_full_unstemmed | Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm |
| title_short | Banana Pseudostem Visual Detection Method Based on Improved YOLOV7 Detection Algorithm |
| title_sort | banana pseudostem visual detection method based on improved yolov7 detection algorithm |
| topic | deep learning banana pseudostem object detection YOLOV7 |
| url | https://www.mdpi.com/2073-4395/13/4/999 |
| work_keys_str_mv | AT liyuancai bananapseudostemvisualdetectionmethodbasedonimprovedyolov7detectionalgorithm AT jingmingliang bananapseudostemvisualdetectionmethodbasedonimprovedyolov7detectionalgorithm AT xingxu bananapseudostemvisualdetectionmethodbasedonimprovedyolov7detectionalgorithm AT jieliduan bananapseudostemvisualdetectionmethodbasedonimprovedyolov7detectionalgorithm AT zhouyang bananapseudostemvisualdetectionmethodbasedonimprovedyolov7detectionalgorithm |