Vehicle target detection method based on improved YOLO V3 network model

Zhang, Qirong; Han, Zhong; Zhang, Yu

doi:10.7717/peerj-cs.1673

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Liu et al [11] based their model on the Faster R-CNN network, modified the anchor point size, and detected corn male tassels by replacing different backbone feature extraction networks, and they concluded that a Residual Neural Network (ResNet) is better than a Visual Geometry Group Network as a feature extraction network for corn male tassels, but a large number of parameters and FLOPs can lead to a slow detection speed in Faster R-CNN. Hongming Zhang et al [12] took maize seedlings as the target, added lightweight improvement measures, and proposed a convolutional neural network detection network for seedling acquisition, which realized the acquisition of maize seedling plants at high throughput and completed the prediction of yield assessment. Liang et al [13] applied several mainstream detection models such as Faster R-CNN [14], SSD [15], and YOLOv3 [16] to train and predict the labeled corn tassel dataset and compared the results.…”

Section: Introductionmentioning

confidence: 99%

ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field

Wu,

Zhang,

Zhou

et al. 2024

Agronomy

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The intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment. In order to realize accurate detection and density assessment of maize male tassels in complex field environments, this study used a UAV to collect images of maize male tassels under different environmental factors in the experimental field and then constructed and formed the ESG-YOLO detection model based on the YOLOv7 model by using GELU as the activation function instead of the original SiLU and by adding a dual ECA attention mechanism and an SPD-Conv module. And then, through the model to identify and detect the male tassel, the model’s average accuracy reached a mean value (mAP) of 93.1%; compared with the YOLOv7 model, its average accuracy mean value (mAP) is 2.3 percentage points higher. Its low-resolution image and small object target detection is excellent, and it can be more intuitive and fast to obtain the maize male tassel density from automatic identification surveys. It provides an effective method for high-precision and high-efficiency identification of maize male tassel phenotypes in the field, and it has certain application value for maize growth potential, yield, and density assessment.

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Section: Introductionmentioning

confidence: 99%

ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field

Wu,

Zhang,

Zhou

et al. 2024

Agronomy

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A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information

Li,

Sun,

Zhang

2024

Sensors

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As a typical component of remote sensing signals, remote sensing image (RSI) information plays a strong role in showing macro, dynamic and accurate information on the earth’s surface and environment, which is critical to many application fields. One of the core technologies is the object detection (OD) of RSI signals (RSISs). The majority of existing OD algorithms only consider medium and large objects, regardless of small-object detection, resulting in an unsatisfactory performance in detection precision and the miss rate of small objects. To boost the overall OD performance of RSISs, an improved detection framework, I-YOLO-V5, was proposed for OD in high-altitude RSISs. Firstly, the idea of a residual network is employed to construct a new residual unit to achieve the purpose of improving the network feature extraction. Then, to avoid the gradient fading of the network, densely connected networks are integrated into the structure of the algorithm. Meanwhile, a fourth detection layer is employed in the algorithm structure in order to reduce the deficiency of small-object detection in RSISs in complex environments, and its effectiveness is verified. The experimental results confirm that, compared with existing advanced OD algorithms, the average accuracy of the proposed I-YOLO-V5 is improved by 15.4%, and the miss rate is reduced by 46.8% on the RSOD dataset.

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YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism

Zhao,

Fu,

Jia

et al. 2024

Sensors

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In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features. Second, we incorporate Omni-directional Dynamic Convolution (ODConv) into the head network, which is capable of simultaneously learning complementary attention across the four dimensions of the kernel space, therefore facilitating the capture of multifaceted features from the input data. Lastly, we introduce Shape-IOU, a new loss function that significantly enhances the accuracy and robustness of detection results for vehicles of varying sizes. Experimental evaluations conducted on the UA-DETRAC dataset demonstrate that our model achieves improvements of 4.7 and 4.4 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, compared to the baseline model. Furthermore, comparative experiments on the SODA10M dataset corroborate the superiority of our method in terms of precision and accuracy.

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Vehicle target detection method based on improved YOLO V3 network model

Cited by 3 publications

References 7 publications

ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field

ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field

A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information

YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism

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