Improved object detection algorithm for drone-captured dataset based on yolov5

Ding, Kaiwen; Li, Xianjiang; Guo, Weijie; Wu, Liaoni

doi:10.1109/iccece54139.2022.9712813

Cited by 23 publications

(12 citation statements)

References 11 publications

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“…The Head network of YOLOv5 is the same as the Head network of YOLOv3. The bounding box loss function used at the output end of YOLOv5 is the CIoU Loss function, which further introduces the concept of corner distance based on GIoU, effectively alleviating the impact of rotation and tilt on target detection performance, thereby improving the model's performance (Ding et al, 2022).…”

Section: Yolov5 Network Structurementioning

confidence: 99%

An improved algorithm based on YOLOv5 for detecting Ambrosia trifida in UAV images

Xiaoming,

Tianzeng,

Haomin

et al. 2024

Front. Plant Sci.

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A YOLOv5-based YOLOv5-KE unmanned aerial vehicle (UAV) image detection algorithm is proposed to address the low detection accuracy caused by the small size, high density, and overlapping leaves of Ambrosia trifida targets in UAV images. The YOLOv5-KE algorithm builds upon the YOLOv5 algorithm by adding a micro-scale detection layer, adjusting the hierarchical detection settings based on k-Means for Anchor Box, improving the loss function of CIoU, reselecting and improving the detection box fusion algorithm. Comparative validation experiments of the YOLOv5-KE algorithm for Ambrosia trifida recognition were conducted using a self-built dataset. The experimental results show that the best detection accuracy of Ambrosia trifida in UAV images is 93.9%, which is 15.2% higher than the original YOLOv5. Furthermore, this algorithm also outperforms other existing object detection algorithms such as YOLOv7, DC-YOLOv8, YOLO-NAS, RT-DETR, Faster RCNN, SSD, and Retina Net. Therefore, YOLOv5-KE is a practical algorithm for detecting Ambrosia trifida under complex field conditions. This algorithm shows good potential in detecting weeds of small, high-density, and overlapping leafy targets in UAV images, it could provide technical reference for the detection of similar plants.

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Section: Yolov5 Network Structurementioning

confidence: 99%

An improved algorithm based on YOLOv5 for detecting Ambrosia trifida in UAV images

Xiaoming,

Tianzeng,

Haomin

et al. 2024

Front. Plant Sci.

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“…Taking an input image with size 3*640*640 as an example, the image is first cut into four 3*320*320 images using the slicing operation, after which the images are spliced through the Concat module [8] . (2) CBL module: CBL is a convolutional block and consists of three network layers, namely the convolution (Conv) operation, the batch normalization (BN) operation, and the activation function (Leaky-relu) [9] . The structure of the CBL module is shown in Fig 5.…”

Section: Architecture Of Yolov5 Networkmentioning

confidence: 99%

Detection and Application of Apple Leaf Diseases Based on Object Detection

2023

FSST

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The healthy development of apple leaves is closely related to apple yield. There are three common symptoms of apple leaves: leaf spot, leaf rust and powdery mildew. Apple leaves are very susceptible to these viruses and are highly infectious. If the disease management of apple leaves is improper, it will lead to heavy losses in apple production and directly affect the economic income of farmers. Early detection of apple leaf diseases is the basis for timely prevention, which can inhibit the spread of diseases and minimize serious economic losses. Nowadays, the existing target detection technology can be used in apple leaf detection. Therefore, this paper will use the yolov5 algorithm based on deep learning to detect apple leaves and determine whether apple leaves are diseased. The experimental data comes from the Kaggle AI Challenge competition data set and the public data set on Kaggle. The disease area is manually marked by labeling tools to form a training apple leaf disease data set. In data processing, the Mosaic data enhancement method is used for data expansion, and the image is transferred into the HSV color space for tone transformation, so that the disease features have better highlights and achieve the purpose of data enhancement. The experimental model uses the YOLOv5 s network model. In order to improve the performance of the model, the CBAM attention mechanism is added to the backbone network of YOLOv5 s. The experimental data show that the accuracy (P) of the YOLOv5 s network model is 0.849, the recall rate ( R ) is 0.788, and the average detection accuracy (mAP) is 0.843. The accuracy (P) of YOLOv5s + CBAM was 0.869, the recall rate (R) was 0.798, and the average detection accuracy (mAP) was 0.848. At the end of the experiment, the web application design based on Flask framework is designed. In the last chapter, this paper looks forward to the future and summarizes the experiment.

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“…Among the above four attention mechanisms, CBAM was chosen to applied to shelter detection because of its simplicity and effectivity. It is a lightweight module and can be trained in an end-to-end manner [31][32][33][34][35]. CBAM combines the attention mechanism of feature channel and feature space.…”

Section: Attention Mechanism Applicationmentioning

confidence: 99%

Shelter identification for shelter-transporting AGV based on improved target detection model yolov5

Yang¹,

Chen²,

Wu³

et al. 2022

Preprint

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Shelter identification is the fundamental issue to make shelter-transporting automated guided vehicle (AGV) effectively detect and transport shelter. Actively identifying shelter has an important problem of high accuracy but slow speed for a complex model, and fast speed but low accuracy for a simple model. However, all kinds of target detection algorithms available have low detection accuracy and speed. In this paper, the model yolov5n6* is developed based on the modified yolov5 model by selecting different model structures, introducing an attention mechanism, and improving loss function and non-maximum suppression (NMS). Then, the experiments for shelter recognition were carried out using the model yolov5n6*. The experimental results show that the box_loss is reduced by 1.2%, the mAP_0.5:0.95 is improved by 2%, and the detection accuracy is improved by 0.87% for the improved model yolov5n6* compared with the yolov5n6. However, the yolov5n6* size is only 7.2M, and the detection time is increased by 0.2ms. So it is proved that the modified model yolov5n6* not only has a significant improvement in the shelter detection ability but also has strong robustness, which meets both the requirements of the recognition accuracy and the detection speed.

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Improved object detection algorithm for drone-captured dataset based on yolov5

Cited by 23 publications

References 11 publications

An improved algorithm based on YOLOv5 for detecting Ambrosia trifida in UAV images

An improved algorithm based on YOLOv5 for detecting Ambrosia trifida in UAV images

Detection and Application of Apple Leaf Diseases Based on Object Detection

Shelter identification for shelter-transporting AGV based on improved target detection model yolov5

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