Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

Fan, Youchen; Qiu, Qianlong; Hou, Shunhu; Li, Yuhai; Xie, Jiaxuan; Qin, Mingyu; Chu, Feihuang

doi:10.3390/electronics11152344

Cited by 22 publications

(11 citation statements)

References 24 publications

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“…Ref. [ 56 ] refines YOLOv5 for vehicle detection in aerial infrared images, ensuring robustness against challenges like occlusion and low contrast.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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Advanced driver assistance systems (ADASs) are becoming increasingly common in modern-day vehicles, as they not only improve safety and reduce accidents but also aid in smoother and easier driving. ADASs rely on a variety of sensors such as cameras, radars, lidars, and a combination of sensors, to perceive their surroundings and identify and track objects on the road. The key components of ADASs are object detection, recognition, and tracking algorithms that allow vehicles to identify and track other objects on the road, such as other vehicles, pedestrians, cyclists, obstacles, traffic signs, traffic lights, etc. This information is then used to warn the driver of potential hazards or used by the ADAS itself to take corrective actions to avoid an accident. This paper provides a review of prominent state-of-the-art object detection, recognition, and tracking algorithms used in different functionalities of ADASs. The paper begins by introducing the history and fundamentals of ADASs followed by reviewing recent trends in various ADAS algorithms and their functionalities, along with the datasets employed. The paper concludes by discussing the future of object detection, recognition, and tracking algorithms for ADASs. The paper also discusses the need for more research on object detection, recognition, and tracking in challenging environments, such as those with low visibility or high traffic density.

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“…Ref. [ 56 ] refines YOLOv5 for vehicle detection in aerial infrared images, ensuring robustness against challenges like occlusion and low contrast.…”

Section: Discussion—methodologymentioning

confidence: 99%

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Malligere Shivanna,

Guo

2023

Sensors

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“…Model comparison before and after the CA module was embedded in the CSP structure (A) Before improvement; (B) After improvement. the GIOU Loss frame loss function as the deviation index of the prediction frame deviation (Fan et al, 2022). EIOU Loss function included three parts: overlap loss, center distance loss and width height loss.…”

Section: Improved Border Regression Loss Functionmentioning

confidence: 99%

Recognition of soybean pods and yield prediction based on improved deep learning model

Guan

et al. 2023

Front. Plant Sci.

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As a leaf homologous organ, soybean pods are an essential factor in determining yield and quality of the grain. In this study, a recognition method of soybean pods and estimation of pods weight per plant were proposed based on improved YOLOv5 model. First, the YOLOv5 model was improved by using the coordinate attention (CA) module and the regression loss function of boundary box to detect and accurately count the pod targets on the living plants. Then, the prediction model was established to reliably estimate the yield of the whole soybean plant based on back propagation (BP) neural network with the topological structure of 5-120-1. Finally, compared with the traditional YOLOv5 model, the calculation and parameters of the proposed model were reduced by 17% and 7.6%, respectively. The results showed that the average precision (AP) value of the improved YOLOv5 model reached 91.7% with detection rate of 24.39 frames per millisecond. The mean square error (MSE) of the estimation for single pod weight was 0.00865, and the average coefficients of determination R2 between predicted and actual weight of a single pod was 0.945. The mean relative error (MRE) of the total weight estimation for all potted soybean plant was 0.122. The proposed method can provide technical support for not only the research and development of the pod’s real-time detection system, but also the intelligent breeding and yield estimation.

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“…Fan et al [19] proposed an infrared vehicle target detection algorithm based on an improved version of YOLOv5. The algorithm used the DenseBlock module to increase shallow feature extraction ability, and the Ghost convolution layer replaced the ordinary convolution layer to improve network feature extraction ability.…”

Section: Computer Visionmentioning

confidence: 99%

Advanced Machine Learning Applications in Big Data Analytics

2023

Electronics

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Application of Improved YOLOv5 in Aerial Photographing Infrared Vehicle Detection

Cited by 22 publications

References 24 publications

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

Recognition of soybean pods and yield prediction based on improved deep learning model

Advanced Machine Learning Applications in Big Data Analytics

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