Improved YOLOX-Tiny network for detection of tobacco brown spot disease

Lin, Jie; Yu, Dianzhi; Pan, Renyong; Cai, Jitong; Liu, Jiaming; Zhang, Licai; Wen, Xingtian; Peng, Xishun; Cernava, Tomislav; Oufensou, Safa; Migheli, Quirico; Chen, Xiaoyulong; Zhang, Xin

doi:10.3389/fpls.2023.1135105

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…Among the same type of one-stage detection algorithms, the detection accuracy of our method is 9.13% higher than that of the deconvolutional SSD (DSSD) [40] (78.6% mAP), 7.65% higher than that of YOLOv3 [11] (82.08% mAP), and 1.19% higher than that of YOLOx-tiny [41] (86.54 mAP). Our method is not as fast as some of the one-stage detection algorithms, but it is significantly better in terms of detection accuracy.…”

Section: Results Obtained On Voc 2007mentioning

confidence: 93%

An improved YOLO algorithm with multisensing for pedestrian detection

Gong,

Wang,

Huang

et al. 2024

SIViP

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Although pedestrian detection techniques are improving, this task is still challenging due to the problems of target occlusion, small targets, and complex pedestrian backgrounds in images of different scenes. As a result, the You Only Look Once (YOLO) algorithm exhibits lower detection accuracy. In this paper, the use of multiple dilated convolutions to sample feature images is proposed avoid the information loss incurred repeated sampling, to improve the feature extraction and target detection performance of the algorithm.In addition, a lightweight shuffle-based efficient channel attention (SECA) mechanism is introduced to conduct grouping in the channel dimension and perform parallel processing for each subfeature map channel. A new branch is introduced to enrich the channel feature information for multiscale feature representation. Finally, a distance intersection over union-based nonmaximum suppression (DIoU-NMS) method is introduced to minimize the occurrence of missed targets due to occlusion by taking the prediction box and ground truth box centroid locations information into account without increasing the computational cost over that of normal NMS. Our method is extensively evaluated on several challenging pedestrian detection datasets, achieving 87.73%, 34.7%, 93.96% and 95.23% mean average precision (mAP) values on PASCAL VOC 2012, MS COCO, Caltech Pedestrian and INRIA Person, which are respectively. The experimental results demonstrate the effectiveness of the method.

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Section: Results Obtained On Voc 2007mentioning

confidence: 93%

An improved YOLO algorithm with multisensing for pedestrian detection

Gong,

Wang,

Huang

et al. 2024

SIViP

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“…In order to obtain better generalization and robustness and avoid overfitting problems, DL models usually need a lot of data as support ( Lin et al., 2023 ). Therefore, three different online data augmentation methods are used in this paper, namely Cutout (DeVries and Taylor, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Improved EfficientNet for corn disease identification

Cai,

Pan,

Lin

et al. 2023

Front. Plant Sci.

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IntroductionCorn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.MethodsTo address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.ResultsCompared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.DiscussionOverall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control.

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“…These values marked improvements of 4.58%, 5%, and 4.78%, respectively, compared to YOLOv5s. ( Lin et al., 2023 ) introduced an enhanced YOLOX-Tiny network, denoted as YOLO-Tobacco, designed for detecting brown spot disease in open-field tobacco crop images. Their objective was to uncover crucial disease features and improve the fusion of diverse feature levels, facilitating the detection of dense disease spots across various scales.…”

Section: Literature Reviewmentioning

confidence: 99%

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

Uddin,

Mazumder,

Prity

et al. 2024

Front. Plant Sci.

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Cauliflower cultivation plays a pivotal role in the Indian Subcontinent’s winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely ‘Bacterial Soft Rot’, ‘Downey Mildew’ and ‘Black Rot’ are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability

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Improved YOLOX-Tiny network for detection of tobacco brown spot disease

Cited by 13 publications

References 34 publications

An improved YOLO algorithm with multisensing for pedestrian detection

An improved YOLO algorithm with multisensing for pedestrian detection

Improved EfficientNet for corn disease identification

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8

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