Research on parking space detection algorithm in complex environments based on improved YOLOv7

Wang, Wanqi; Zhang, Wei; Zhang, Hong; Zhang, Anyu

doi:10.1088/1361-6501/ad0b68

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…YOLOv7 [20] is the latest YOLO structure in the YOLO family, which is extremely fast and can operate in the range of 5 frames per second to 160 frames per second, while maintaining high accuracy. The YOLOv7 offers significant advantages over other known target detectors in terms of both speed and accuracy.…”

Section: Key Point Detection Algorithm Based On Yolov7posementioning

confidence: 99%

Measurement of the Angle between stems and leaves of rice based on key point detection

Seng,

Liu,

Yang

et al. 2024

JCEIM

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Stem-leaf angle is an important phenotypic parameter of rice, which is crucial for the growth and development of rice plants and scientific breeding. However, the traditional method is inefficient and subjective. In order to quickly and accurately calculate the stem-leaf pinch angle of rice, this paper proposes a key point-based stem-leaf pinch angle detection method. Experimentally, four periods of rice (Jointing stage, Booting stage, Heading stage and Mature stage) are selected as research objects, image data are collected, key points are extracted by YOLOv7-pose network model, and a lightweight convolution method GSConv is introduced, which is able to reduce the complexity of the model and improve the accuracy of the key point detection, the average accuracy of key point detection was improved from 93.7% to 98.7%, an improvement of 5%. Finally, the angular size of the pinch angle was determined based on the coordinates of the key points located by the model, and the R2 of the pinch angle calculation results to the actual manual measurements was the highest of 0.84, the errors for the four periods were 1.543°, 1.716°, 1.213°, and 0.998°. So the method ensures the accuracy of identification while the model size is reduced, and can effectively measure the angle of rice stem and leaf pinch, and the investigated method can better help farmers to understand the growth and development of plants and provide support for the selection of excellent crop traits.

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Section: Key Point Detection Algorithm Based On Yolov7posementioning

confidence: 99%

Measurement of the Angle between stems and leaves of rice based on key point detection

Seng,

Liu,

Yang

et al. 2024

JCEIM

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Camellia oleifera trunks detection and identification based on improved YOLOv7

Wang,

Liu,

Luo

et al. 2024

Concurrency and Computation

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SummaryCamellia oleifera typically thrives in unstructured environments, making the identification of its trunks crucial for advancing agricultural robots towards modernization and sustainability. Traditional target detection algorithms, however, fall short in accurately identifying Camellia oleifera trunks, especially in scenarios characterized by small targets and poor lighting. This article introduces an enhanced trunk detection algorithm for Camellia oleifera based on an improved YOLOv7 model. This model incorporates dynamic snake convolution instead of standard convolutions to bolster its feature extraction capabilities. It integrates more contextual information, thus enhancing the model's generalization ability across various scenes. Additionally, coordinate attention is introduced to refine the model's spatial feature representation, amplifying the network's focus on essential target region features, which in turn boosts detection accuracy and robustness. This feature selectively strengthens response levels across different channels, prioritizing key attributes for classification and localization. Moreover, the original coordinate loss function of YOLOv7 is replaced with EIoU loss, further enhancing the model's robustness and convergence speed. Experimental results demonstrate a recall rate of 96%, a mean average precision (mAP) of 87.9%, an F1 score of 0.87, and a detection speed of 18 milliseconds per frame. When compared with other models like Faster‐RCNN, YOLOv3, ScaledYOLOv4, YOLOv5, and the original YOLOv7, our improved model shows mAP increases of 8.1%, 7.0%, 7.5%, and 6.6% respectively. Occupying only 70.8 MB, our model requires 9.8 MB less memory than the original YOLOv7. This model not only achieves high accuracy and detection efficiency but is also easily deployable on mobile devices, providing a robust foundation for future intelligent harvesting technologies.

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FR-YOLOv7: feature enhanced YOLOv7 for rotated small object detection in aerial images

Tang,

Deng,

Liu

et al. 2024

Meas. Sci. Technol.

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For the problems of weak object feature expression and variable object orientation in aerial image object detection, this paper proposed a feature enhanced YOLOv7 for rotated small object detection in aerial images. Firstly, for the problem of feature loss in the feature extraction stage, the feature enhanced spatial pyramid pooling and cross stage partial connections (FE-SPPCSPC) module was proposed, which effectively boost the feature expression of small object. Secondly, an attention guided max-pooling (AG-MP) module was constructed to address the problem of feature loss. Then, the rotated object detection head was introduced to solve the problem of false negatives caused by variable object angles and dense object distribution. Finally, a multi-scale loss function was proposed for improving the detection effects of rotated small objects detection in aerial images. Extensive experiments were conduct on the public datasets of DOTA and UCAS-AOD, with the help of the proposed method, we can achieve the detection accuracy that the mean Average Precision(mAP) are 79.7% and 98.9%, respectively. Experimental results demonstrate that the proposed method has a significant improvement on the detection of small targets in aerial images.

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Research on parking space detection algorithm in complex environments based on improved YOLOv7

Cited by 4 publications

References 32 publications

Measurement of the Angle between stems and leaves of rice based on key point detection

Measurement of the Angle between stems and leaves of rice based on key point detection

Camellia oleifera trunks detection and identification based on improved YOLOv7

FR-YOLOv7: feature enhanced YOLOv7 for rotated small object detection in aerial images

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