Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

Abdulghani, Abdulghani M.; Abdulghani, Mokhles M.; Walters, Wilbur L.; Abed, Khalid H.

doi:10.32604/jai.2023.041341

Cited by 6 publications

(2 citation statements)

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“…The YOLO algorithms' series has become a widely used algorithm as a one-step algorithm for object detection [46]. In this study, we used YOLOV8 as the baseline network.…”

Section: Methodsmentioning

confidence: 99%

Automated Counting of Tobacco Plants Using Multispectral UAV Data

Lin,

Chen,

Qiang

et al. 2023

Agronomy

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Plant counting is an important part in precision agriculture (PA). The Unmanned Aerial Vehicle (UAV) becomes popular in agriculture because it can capture data with higher spatiotemporal resolution. When it is equipped with multispectral sensors, more meaningful multispectral data is obtained for plants’ analysis. After tobacco seedlings are raised, they are transplanted into the field. The counting of tobacco plant stands in the field is important for monitoring the transplant survival rate, growth situation, and yield estimation. In this work, we adopt the object detection (OD) method of deep learning to automatically count the plants with multispectral images. For utilizing the advanced YOLOv8 network, we modified the architecture of the network to adapt to the different band combinations and conducted extensive data pre-processing work. The Red + Green + NIR combination obtains the best detection results, which reveal that using a specific band or band combinations can obtain better results than using the traditional RGB images. For making our method more practical, we designed an algorithm that can handling the image of a whole plot, which is required to be watched. The counting accuracy is as high as 99.53%. The UAV, multispectral data combined with the powerful deep learning methods show promising prospective in PA.

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“…The YOLO algorithms' series has become a widely used algorithm as a one-step algorithm for object detection [46]. In this study, we used YOLOV8 as the baseline network.…”

Section: Methodsmentioning

confidence: 99%

Automated Counting of Tobacco Plants Using Multispectral UAV Data

Lin,

Chen,

Qiang

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…Its core goal is to return the category and location of the target through a network. YOLO (You Only Look Once, YOLO) series algorithms have good comprehensive performance [1] [2]. Yolo uses a convolutional network to extract features, and then uses a fully connected layer to obtain predicted values.…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Method based on YOLOV5s Target Detection Model

Dong,

Li,

Zhou

2023

FCIS

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Aiming at the problem of low detection accuracy of small targets, an object detection method based on average pooling improved YOLOV5s model is proposed. The algorithm introduces the Squeeze Excitation attention module and the Efficient Intersection Over Union loss function to comprehensively improve the detection calculation efficiency and accurate deployment ability. With the development of deep learning technology, which is of great significance to improve the detection accuracy and detection rate. YOLO greatly improves detection performance, three times faster than retinanet and 2 times faster than faster-rcnn. YOLO has strong generalization ability, can be applied to different application scenarios, and is also easy to deploy. The steel surface defect public dataset was selected for verification. The results show that the improved YOLOV5s model is better than the original YOLOV5s model, the test average accuracy mAP can reach 81.8%, the average accuracy mAP of the model is increased by 7.4%, and the overall performance of the improved model is better than other conventional models.

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