Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm

Zhao, Wentao; Wu, Dasheng; Zheng, Xinyu

doi:10.3390/s23094234

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…To verify the superior performance of the improved Yolov5 model, we measured the mAP, FPS, model volume, etc. Some commonly used metrics of precision (P), recall (R), average precision (AP), F1 Score (F1), and mean average precision (mAP) were selected to evaluate the model performance [36], and the metrics were defined as follows:…”

Section: Loss Function and Model Evaluation Metricsmentioning

confidence: 99%

Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5

Wang

Zhang

Yang

et al. 2023

Sensors

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It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection.

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Section: Loss Function and Model Evaluation Metricsmentioning

confidence: 99%

Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5

Wang

Zhang

Yang

et al. 2023

Sensors

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“…The cross-attention module was used to detect relevant feature information. In another notable work, Zhao et al [23] integrated a coordinate attention module into the backbone of the You Only Look Once (YOLOv5s) model to detect small buds and occluded flowers in real-field conditions effectively.…”

Section: Introductionmentioning

confidence: 99%

An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions

Noon,

Amjad,

Qureshi

et al. 2024

AgriEngineering

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Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.

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“…Xuehu Duan et al put forward the DBF-YOLO algorithm to improve the accuracy of small target detection [24]. Wentao Zhao et al came up with CR-YOLOv5s to detect the flowering time of chrysanthemums [25], and so on. Researchers are enthusiastic about YOLOv5.…”

Section: Introductionmentioning

confidence: 99%

UN-YOLOv5s: A UAV-Based Aerial Photography Detection Algorithm

Guo

Liu

et al. 2023

Sensors

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With the progress of science and technology, artificial intelligence is widely used in various disciplines and has produced amazing results. The research of the target detection algorithm has significantly improved the performance and role of unmanned aerial vehicles (UAVs), and plays an irreplaceable role in preventing forest fires, evacuating crowded people, surveying and rescuing explorers. At this stage, the target detection algorithm deployed in UAVs has been applied to production and life, but making the detection accuracy higher and better adaptability is still the motivation for researchers to continue to study. In aerial images, due to the high shooting height, small size, low resolution and few features, it is difficult to be detected by conventional target detection algorithms. In this paper, the UN-YOLOv5s algorithm can solve the difficult problem of small target detection excellently. The more accurate small target detection (MASD) mechanism is used to greatly improve the detection accuracy of small and medium targets, The multi-scale feature fusion (MCF) path is combined to fuse the semantic information and location information of the image to improve the expression ability of the novel model. The new convolution SimAM residual (CSR) module is introduced to make the network more stable and focused. On the VisDrone dataset, the mean average precision (mAP) of UAV necessity you only look once v5s(UN-YOLOv5s) is 8.4% higher than that of the original algorithm. Compared with the same version, YOLOv5l, the mAP is increased by 2.2%, and the Giga Floating-point Operations Per Second (GFLOPs) is reduced by 65.3%. Compared with the same series of YOLOv3, the mAP is increased by 1.8%, and GFLOPs is reduced by 75.8%. Compared with the same series of YOLOv8s, the detection accuracy of the mAP is improved by 1.1%.

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Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm

Cited by 4 publications

References 27 publications

Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5

Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5

An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions

UN-YOLOv5s: A UAV-Based Aerial Photography Detection Algorithm

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