Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm

Hong, Weiwei; Ma, Zenghong; Ye, Bingliang; Yu, Guo; Tang, Tao; Zheng, Mingming

doi:10.3390/s23031562

Cited by 26 publications

(17 citation statements)

References 27 publications

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“…In this system, for the training optimizer, we utilize stochastic gradient descend (Amari et al, 1993). This optimizer has been well utilized by prior studies (Hong et al, 2023;Yang et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Based Brain Tumor Localization Using YOLOv5

Sadrawi¹,

Fugaha²,

Heerlie³

et al. 2023

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Brain tumor is a mutation in the brain cells in which the cells keep dividing. The earlier the tumor detected, the higher survival rate for the patient. This study develops the brain tumor detection system by utilizing the you only look once (YOLO). The model is based on YOLOv5 architect. The open dataset of tumorous images is utilized. From this dataset, the corresponding masks are given alongside the images. Our study tries to compare several YOLOv5 models to localize the brain tumor. The results show YOLOv5m, YOLOv5l, and YOLOv5x models have higher precision and recall values. The inference time from those models is relatively small for recent computational resources. In conclusion, the YOLOv5 models have produced superior result in localizing the brain tumor

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence Based Brain Tumor Localization Using YOLOv5

Sadrawi¹,

Fugaha²,

Heerlie³

et al. 2023

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“…To better fuse high-level and low-level features, reduce computation costs, and improve model robustness, this paper adopts the weighted bidirectional feature pyramid (BiFPN) structure [16] . It simplifies the PAN structure, and fuses multiscale feature information while establishing bidirectional connections between feature maps of the same scale to address the problem of feature information loss to some extent [17] . The feature pyramid structure is shown in Figure 6.…”

Section: Feature Pyramid Optimizationmentioning

confidence: 99%

Yolov5 Vehicle Detection Model in Fog Based on Channel Attention Enhancement

Haoran,

Li,

Yin

et al. 2023

J. Phys.: Conf. Ser.

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Vehicle detection in foggy weather plays an indispensable role in the field of intelligent transportation. This article proposes an improved YOLOv5 vehicle detection model based on the problems of insufficient detection accuracy and high fault tolerance of most algorithms in foggy weather. First, the AOD-Net network is used for defogging preprocessing of the original image. Then, the SE attention mechanism is fused in the C3 module of the Backbone feature extraction backbone network to adaptively allocate weight information, enhance the attention to important features, and reduce the impact of noise and irrelevant information. Finally, BiFPN is used in the Neck feature fusion network to replace the original PANet and enhance the model’s feature fusion ability. Experiments are conducted on the Cityscapes and RTTS datasets, and the results show that the improved YOLOv5 algorithm in this article has significant improvements in precision, recall rate, and average precision mean compared to the original model, with increases of 8.4%, 9.5%, and 9.2%, respectively. It can better adapt to vehicle detection tasks in foggy weather.

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“…With the continuous development of single-stage target detection algorithms, the YOLO family of iterations has been continuously updated, from the initial YOLOv1 [8,9] and YOLOv2 [10,11] to the faster inference YOLOv5 [12][13][14][15], to the higher accuracy YOLOv7 [16][17][18][19], and all the way up to the current YOLOv8. The YOLO algorithms have made significant progress in terms of both the average accuracy as well as the inference speed have been significantly improved, and the lightweighting of the model has also been achieved, and these advances reflect the potential and prospect of the YOLO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Steel surface defect detection algorithm based on ESI-YOLOv8

Zhang,

Wang,

Fang

2024

Mater. Res. Express

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To enhance the precision of detecting defects on steel plate surfaces and diminish the incidences of false detection and leakage, the ESI-YOLOv8 algorithm is introduced. The algorithm incorporates an EP module, which minimizes redundant computations and model parameters to optimize efficiency and simultaneously increases the multi-scale fusion mechanism to expand the sensory field. The SPPF-LSKA module is proposed by integrating the large separated convolutional attention module with the spatial pyramid pooling module. This integration reduces computational complexity, accelerates model operation speed, and improves detection accuracy. Additionally, the INNER-CIOU loss function is introduced to improve detection speed and model accuracy by controlling the scale size of the auxiliary border. The results of the experiment indicate that, following the improvements made, the algorithm's detection accuracy has increased to 78%, which is 3.7% higher than the original YOLOv8. Furthermore, the model parameters were reduced, and the verification was conducted using the CoCo dataset, resulting in an average accuracy of 77.8%. In conclusion, the algorithm has demonstrated its ability to perform steel plate surface defect detection with efficiency and accuracy.

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Detection of Green Asparagus in Complex Environments Based on the Improved YOLOv5 Algorithm

Cited by 26 publications

References 27 publications

Artificial Intelligence Based Brain Tumor Localization Using YOLOv5

Artificial Intelligence Based Brain Tumor Localization Using YOLOv5

Yolov5 Vehicle Detection Model in Fog Based on Channel Attention Enhancement

Steel surface defect detection algorithm based on ESI-YOLOv8

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