Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism

Mahmood, Mohammed Thakır; Ahmed, Saadaldeen Rashid; Ahmed, Mohammed Rashid

doi:10.1088/1757-899x/928/2/022027

Cited by 32 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convolutional AB Network is a television network that broadcasts in the Many papers investigate the difficulty of diagnosing diseases, and many publications focus on improving diagnostic processes. [19]. We claim 88.4 percent accuracy for a 5-row clustering problem in our experience.…”

Section: [Deep Ab] [Deep Ab] [Deep Ab] [Deep 12]mentioning

confidence: 68%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

View full text Add to dashboard Cite

Cardiac disease, including atrial fibrillation (AF), is one of the biggest causes of morbidity and mortality in the world, accounting for one third of all deaths. Cardiac modelling is now a well-established field. The Convolutional Neural Network (CNN) algorithm offer a valuable way of gaining insight into the dynamic behaviors of the heart, in normal and pathological conditions. Great efforts have been put into modelling the ventricles, whilst the atria have received less focus. This research therefore concentrates on developing models of the heart ECG atria using deep learning. The research developed an experimental result on MIT-BIH dataset for modelling myocyte electrophysiology and excitation waves in 1D & 2D tissues. It includes optimizations such as adaptive stimulus protocols. As examples of application, it is used to investigate effects of a novel anion bearing current on heart atrial excitation and the effect of remodeling on atrial myocyte electrical heterogeneity. A computationally efficient CNN anatomically based model of the heart atria is constructed. The 3D-CNN model includes heterogeneous, biophysically detailed electrophysiology and conduction anisotropy. The full model activates in 121 ms in heart rhythm, in close agreement with clinical ECG data. The model is used, with the toolkit, to investigate the function effects of S140G mutation in MIT-BIH dataset which is associated with familial. The 3D-CNN model forms the core of a boundary element model of the P-wave Body Surface Potential (BSP). The CNN model incorporates representations of the heart blood masses. Generated ECGs show qualitative agreement with clinical data. Their morphology is as expected for a healthy person, with a lead duration of 103 ms. The CNN model is used to verify an existing algorithm for focal atrial tachycardia location and in providing explanation for a novel clinical phenomenon, using CNN with 99.27% accuracy. Models of the human atria and body surface potential are constructed. The models are validated against both experimental and clinical data. These models are suitable to use as the platform for further research.

show abstract

Section: [Deep Ab] [Deep Ab] [Deep Ab] [Deep 12]mentioning

confidence: 68%

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Jabbar¹,

Alwan²,

Ali³

2022

PEN

View full text Add to dashboard Cite

show abstract

“…With the rise of target detection methods based on deep learning, many researchers also explore the application of these methods to infrared target detection. Li et al [18] exploited the YOLOv3 to solve the problem of power equipment detection in the infrared scene, Mahmood et al [23] adjusted the detection head of the YOLOv2 and improved the accuracy for counting the vehicles in infrared monitoring. Shi et al [29] adopted the Faster R-CNN based on a two-stage target detection algorithm to solve the problem of oil leakage detection in infrared images.…”

Section: Related Workmentioning

confidence: 99%

Boosting Infrared Target Detection: Unveiling the Power of ResNet-SA and Detection Block

Lin

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

We propose an IR-YOLOv5 to address the limitations of general target detection approaches in infrared scenes. Specifically, IR-YOLOv5 takes into account common issues present in infrared images, such as low resolution, high noise, low contrast, and small targets. To achieve an efficient interaction of information between different channels and obtain rich semantic information of targets in infrared images, the feature extraction network leverages the attention mechanism. Additionally, the detection scheme of YOLOv5 is enhanced, and a detection head designed for small and weak targets is incorporated to improve the detection probability of these targets in infrared images. Furthermore, the method proposes an efficient PANet target recognition network by designing the Detection Block module to effectively reduce the parameter size and ensure real-time operations of the detector while enhancing the detection accuracy. The proposed method is evaluated on the FLIR infrared autonomous driving dataset and outperforms yolov5 with a 4% improvement in mean Average Precision (mAP) and a reduced model parameter size.

show abstract

“…However, it can be seen that the light imaging system cannot work day and night, and is easily affected by the severe weather conditions such as smoke and dust. The infrared system uses infrared radiation to obtain information with infrared detectors, therefore, it has the characteristics of all-weather operation, good concealment, easy penetration of smoke and dust and strong anti-noise [2][3][4][5]. At present, the target detection in infrared scenes has important applications in the fields of autonomous driving [6,7], video surveillance [8,9], military and other fields [10].…”

Section: Introductionmentioning

confidence: 99%

Research on infrared target detection based on neural network fusion

et al. 2023

View full text Add to dashboard Cite

Infrared technology can detect targets under special weather conditions, such as night, rain and fog. To improve the detection accuracy of vehicles, pedestrians and other targets in infrared images, an infrared target detection algorithm with fusion neural network is proposed. Firstly, we use Ghost convolution to replace the resunit unit of the convolution layer of the deep residual network layer in YOLOv5s, which can reduce the amount of parameters without losing accuracy. Then, the global channel attention (GCA) is added to the upper sampling layer, the detection accuracy of network is further improved by enhancing the characteristics of the overall goal. Also, the Channel Space Attention (CPA) space attention mechanism is added to the output end to obtain more accurate target location information. The infrared data set taken by the UAV is trained and tested. The accuracy rate of detection based on YOLOv5s and fusion neural network is 96.47%, the recall rate is 91.51%, and the F1 score is 94%, which is 7% higher than YOLOv5s. The results show that the target detection rate of infrared images is improved by proposed method, which has strong research value and broad application prospects.

show abstract

Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism

Cited by 32 publications

References 13 publications

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Classification SINGLE-LEAD ECG by using conventional neural network algorithm

Boosting Infrared Target Detection: Unveiling the Power of ResNet-SA and Detection Block

Research on infrared target detection based on neural network fusion

Contact Info

Product

Resources

About