Investigating the efficiency of deep learning based security system in a real-time environment using YOLOv5

Majeed, Fahad; Khan, Farrukh Zeeshan; Nazir, Maria; Iqbal, Zeshan; Alhaisoni, Majed; Tariq, Usman; Khan, Muhammad Attique; Kadry, Seifedine

doi:10.1016/j.seta.2022.102603

Cited by 18 publications

(15 citation statements)

References 23 publications

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“…It is faster and more accurate than the previous versions, and it is also easier to use. From the perspective of both functionality and user-friendliness, v5 is simply the best choice [40]. Most object detection algorithms require some form of labeled data to train the model.…”

Section: Methodsmentioning

confidence: 99%

Image and video-based crime prediction using object detection and deep learning

Boukabous¹,

Azizi²

2023

Bulletin EEI

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In recent years, the use of artificial intelligence (AI) for image and video-based crime detection has gained significant attention from law enforcement agencies and security experts. Indeed, deep learning (DL) models can learn complex patterns from data and help law enforcement agencies save time and resources by automatically identifying and tracking potential criminals. This contributes to make deep investigations and better steer their targets’ searches. Among others, handheld firearms and bladed weapons are the most frequent objects encountered at crime scenes. In this paper, we propose a DL-based surveillance system that can detect the presence of tracked objects, such as handheld firearms and bladed weapons, as well as may proceed to alert authorities regarding eventual threats before an incident occurs. After making a comparison of different DL-based object detection techniques, such as you only look once (YOLO), single shot multibox detector (SSD), or faster region-based convolutional neural networks (R-CNN), YOLO achieves the optimal balance of mean average precision (mAP) and inference speed for real-time prediction. Thus, we retain YOLOv5 for the implementation of our solution.

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Section: Methodsmentioning

confidence: 99%

Image and video-based crime prediction using object detection and deep learning

Boukabous¹,

Azizi²

2023

Bulletin EEI

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“…The CSP1 _ X and CSP2 _ X structures in CSP are applied to Backbone and Neck respectively. CSP solves the network optimization problem of repeated gradient information in the backbone of other large-scale convolutional neural network frameworks, reduces the model size and improves the inference speed and accuracy (Fahad et al ., 2022). Focus is a slicing operation on the feature map, which integrates width and height information into multiple dimensions to improve reasoning speed.…”

Section: Methodsmentioning

confidence: 99%

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Yang

Shou-song

et al. 2023

IJICC

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PurposeFire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approachThis paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.FindingsBased on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.Originality/valueAiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

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“…The YOLOv5 neural network [17], developed by Glen J. Braden et al, is used for real-time target detection within the YOLO (You Only Look Once) series. Among the four structures in terms of network depth and width, YOLOv5s is the smallest and excels in real-time performance.…”

Section: Yolov5mentioning

confidence: 99%

Research on water level measurement technology based on the residual length ratio of image characters

Liu,

Wang,

Huang

et al. 2024

SIViP

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Aiming at the low efficiency and poor adaptability of traditional water level measurement methods, a water level measurement technology based on the residual length ratio of image characters is proposed in this paper. First, by improving YOLOv5, the lightweight MobilenetV3 is used to replace CSPDarkNet53, and the CBAM attention mechanism is introduced to accurately locate the water gauge and the complete "E" character, and obtain the interface area between the residual "E" character and the water. Secondly, by improving U2-Net, the ordinary convolutions of RSU4-RSU7 in the decoding phase are replaced by depth-separable convolutions, and the ECA attention mechanism is introduced to improve the overall inference speed and accuracy to achieve the residual "E" character and the precise segmentation of water bodies. Finally, the water level value is calculated based on the residual length ratio of the characters. The experimental results show that the accuracy of the improved YOLOv5 is 98.12%, the average intersection over union ratio of the improved U2-Net is 86.23%, and the measurement error of water level is less than 1 cm, which meets the requirements of hydrological detection specifications. At the same time, the improved model reduces the number of parameters and computational complexity, which increases the speed of inference.

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Investigating the efficiency of deep learning based security system in a real-time environment using YOLOv5

Cited by 18 publications

References 23 publications

Image and video-based crime prediction using object detection and deep learning

Image and video-based crime prediction using object detection and deep learning

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Research on water level measurement technology based on the residual length ratio of image characters

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