Visual Cognition Inspired Vehicle Re-identification via Correlative Sparse Ranking with Multi-view Deep Features

Sun, Dengdi; Liu, Lidan; Zheng, Aihua; Jiang, Bo; Luo, Bin

doi:10.1007/978-3-030-00563-4_6

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…The YOLOv5 network provided the advantages of being fast, small in size and excellent accuracy over the previous four versions. In the area of object detection, Fast R-CNN [44], SSD [45], YOLO [43] and Faster R-CNN [46] all had appeared. The YOLO algorithm was related to the end-to-end detection approach.…”

Section: Guava Multiple Leaf Disease Detection (Gmldd) Technique On A...mentioning

confidence: 99%

“…In the original YOLOv5 neural network model's structure [46], every trapezoid or rectangle indicated a module enclosed by many neural networks with a single layer that was coupled. The Cross-Stage-Partial connections (CSPs) neural network modules used three (3) convolutions on the feature maps before processing it with four distinct scales of max pooling.…”

Section: Intersection Over Union(iou) = Nintersection Unionmentioning

confidence: 99%

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Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

Rashid¹,

Khan²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

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The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments, soil conditions and higher human consumption. It is cultivated in vast areas of Asian and Non-Asian countries, including Pakistan. The guava plant is vulnerable to diseases, specifically the leaves and fruit, which result in massive crop and profitability losses. The existing plant leaf disease detection techniques can detect only one disease from a leaf. However, a single leaf may contain symptoms of multiple diseases. This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps. Firstly, Guava Infected Patches Modified MobileNetV2 and U-Net (GIP-MU-NET) has been proposed to segment the infected guava patches. The proposed model consists of modified MobileNetv2 as an encoder, and the U-Net model's up-sampling layers are used as a decoder part. Secondly, the Guava Leaf Segmentation Model (GLSM) is proposed to segment the healthy and infected leaves. In the final step, the Guava Multiple Leaf Diseases Detection (GMLDD) model based on the YOLOv5 model detects various diseases from a guava leaf. Two self-collected datasets (the Guava Patches Dataset and the Guava Leaf Diseases Dataset) are used for training and validation. The proposed method detected the various defects, including five distinct classes, i.e., anthracnose, insect attack, nutrition deficiency, wilt, and healthy. On average, the GIP-MU-Net model achieved 92.41% accuracy, the GLSM gained 83.40% accuracy, whereas the proposed GMLDD technique achieved 73.3% precision, 73.1% recall, 71.

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Section: Guava Multiple Leaf Disease Detection (Gmldd) Technique On A...mentioning

confidence: 99%

Section: Intersection Over Union(iou) = Nintersection Unionmentioning

confidence: 99%

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

Rashid¹,

Khan²,

Ali³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…There are many research on visual detection of an intelligent transportation system, such as road detection [13], traffic flow prediction [14], license plate recognition [15] and vehicle recognition [16], but there are few research on helmet detection of motorcyclists. At present, although some motorcycle helmet detection methods have been proposed (refer to Section 2 for details), there are still some problems: (1) Most of the methods use traditional machine learning technology, which is poor in accuracy and speed.…”

Section: Introductionmentioning

confidence: 99%

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Jia

Liang

et al. 2021

IET Image Processing

111

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In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft-NMS instead of NMS. A new motorcycle helmet dataset (HFUT-MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state-of-the-art methods. Our method achieves mAP of 97.7%, F1-score of 92.7% and frames per second (FPS) of 63, which outperforms other state-of-the-art detection methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Discriminative-region attention and orthogonal-view generation model for vehicle re-identification

Wang

Wei

et al. 2022

Appl Intell

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Visual Cognition Inspired Vehicle Re-identification via Correlative Sparse Ranking with Multi-view Deep Features

Cited by 6 publications

References 16 publications

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Discriminative-region attention and orthogonal-view generation model for vehicle re-identification

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