A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models

Gündüz, Mehmet; Işık, Gültekin

doi:10.1007/s11554-023-01276-w

Cited by 32 publications

(7 citation statements)

References 29 publications

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“…In the identification results of citrus ripeness in different environments, the model accurately identifies citrus fruits at distinct ripeness stages. When the YOLO-CIT model is applied to GPU devices, its FPS exceeds 60 and detection accuracy exceeds 80%, indicating that the improved model can be combined with high frame rate cameras to provide real-time position information of different detection targets ( Fang et al., 2019 ; Gündüz and Işık, 2023 ). It can be effectively applied to citrus harvesting robots, laying the foundation for their efficient harvesting operations.…”

Section: Discussionmentioning

confidence: 99%

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model

Wang,

Han,

et al. 2024

Front. Plant Sci.

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Citrus fruits are extensively cultivated fruits with high nutritional value. The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. However, challenges arise in the identification of citrus fruit ripeness due to the similarity in color between green unripe citrus fruits and tree leaves, leading to an omission in identification. Additionally, the resemblance between partially ripe, orange-green interspersed fruits and fully ripe fruits poses a risk of misidentification, further complicating the identification of citrus fruit ripeness. This study proposed the YOLO-CIT (You Only Look Once-Citrus) model and integrated an innovative R-LBP (Roughness-Local Binary Pattern) method to accurately identify citrus fruits at distinct ripeness stages. The R-LBP algorithm, an extension of the LBP algorithm, enhances the texture features of citrus fruits at distinct ripeness stages by calculating the coefficient of variation in grayscale values of pixels within a certain range in different directions around the target pixel. The C3 model embedded by the CBAM (Convolutional Block Attention Module) replaced the original backbone network of the YOLOv5s model to form the backbone of the YOLO-CIT model. Instead of traditional convolution, Ghostconv is utilized by the neck network of the YOLO-CIT model. The fruit segment of citrus in the original citrus images processed by the R-LBP algorithm is combined with the background segment of the citrus images after grayscale processing to construct synthetic images, which are subsequently added to the training dataset. The experiment showed that the R-LBP algorithm is capable of amplifying the texture features among citrus fruits at distinct ripeness stages. The YOLO-CIT model combined with the R-LBP algorithm has a Precision of 88.13%, a Recall of 93.16%, an F1 score of 90.89, a mAP@0.5 of 85.88%, and 6.1ms of average detection speed for citrus fruit ripeness identification in complex environments. The model demonstrates the capability to accurately and swiftly identify citrus fruits at distinct ripeness stages in real-world environments, effectively guiding the determination of picking targets and path planning for harvesting robots.

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Section: Discussionmentioning

confidence: 99%

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model

Wang,

Han,

et al. 2024

Front. Plant Sci.

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“…YOLO is characterized by dividing an image into a grid and simultaneously predicting bounding boxes for objects and their class probabilities within each grid cell. This allows YOLO to exhibit excellent performance in real-time object detection [24], [25].…”

Section: Researchermentioning

confidence: 99%

Development of a Risk Space Prediction Model Based on CCTV Images Using Deep Learning: Crowd Collapse

Chang,

Jung,

Park

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Crowd disasters are not limited to events but can occur at any time and place where large crowds are densely gathered. The most significant factor contributing to injuries in mass gatherings is pressure. The likelihood of injuries due to pressure significantly increases when the crowd density exceeds 5-6 individuals per square meter. Once a crowd disaster occurs, it becomes challenging for rescue and medical personnel to access the affected area, potentially exacerbating the situation. However, since there is no single clear solution to address crowd disasters, there is a need for a system that can detect and analyze them in advance or in real-time. This research aims to contribute to the proactive detection and analysis of various crowd disasters, focusing on improving strategies for disaster response. In this study, we utilized a dataset of 50 videos from 35 individual incidents to propose a framework that classifies the risk levels into Crowd Crush, Crowd Wave, and Crowd Collapse. Additionally, we analyzed the feasibility of real-time detection using P2PNet (Point to Point Network), Yolo (You Only Look Once) v8, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms, moving away from prior still image-based detection methods. Our research outcomes provide new evidence for the feasibility of real-time detection using DBSCAN in Crowd Disaster scenarios. Moreover, the findings from this study can serve as valuable reference material for upcoming research, particularly emphasizing the algorithmic analysis of crowd collapses.

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“…YOLO is the most typical representation of one-stage target detection algorithms, which uses deep neural networks for object recognition and localization, and runs fast enough to be used in real-time systems [36]. YOLOv7 is the more advanced algorithm of the YOLO series, surpassing the previous YOLO versions in terms of accuracy and speed.…”

Section: Yolov7mentioning

confidence: 99%

Research on Blockchain-Based Cereal and Oil Video Surveillance Abnormal Data Storage

Zhang,

Cui,

et al. 2023

Agriculture

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Cereal and oil video surveillance data play a vital role in food traceability, which not only helps to ensure the quality and safety of food, but also helps to improve the efficiency and transparency of the supply chain. Traditional video surveillance systems mainly adopt a centralized storage mode, which is characterized by the deployment of multiple monitoring nodes and a large amount of data storage. It is difficult to guarantee the data security, and there is an urgent need for a solution that can achieve the safe and efficient storage of cereal and oil video surveillance data. This study proposes a blockchain-based abnormal data storage model for cereal and oil video surveillance. The model introduces a deep learning algorithm to process the cereal and oil video surveillance data, obtaining images with abnormal behavior from the monitoring data. The data are stored on a blockchain after hash operation, and InterPlanetary File System (IPFS) is used as a secondary database to store video data and alleviate the storage pressure on the blockchain. The experimental results show that the model achieves the safe and efficient storage of cereal and oil video surveillance data, providing strong support for the sustainable development of the cereal and oil industry.

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A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models

Cited by 32 publications

References 29 publications

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model

Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model

Development of a Risk Space Prediction Model Based on CCTV Images Using Deep Learning: Crowd Collapse

Research on Blockchain-Based Cereal and Oil Video Surveillance Abnormal Data Storage

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