Heat diffusion embedded level set evolution for infrared image segmentation

Wei, Ziwei; Qiao, Yulong; Lu, Xiao

doi:10.1049/iet-ipr.2018.6629

Cited by 2 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Section: Introductionmentioning

confidence: 99%

Research on infrared target detection based on neural network fusion

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, visible‐light imaging requires stable illumination conditions to ensure good performance. In contrast, infrared images are formed by the detector that receives the infrared rays reflected by the target or emitted by the target [4]. The acquisition of the infrared images is not affected by the lighting conditions and has a long detection distance, satisfying the actual requirements in complex environments.…”

Section: Introductionmentioning

confidence: 99%

Road infrared target detection with I‐YOLO

Sun

Zhang

Huang

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

The detection of road infrared targets is essential for autonomous driving. Different from RGB images, the acquisition of infrared images is unaffected by visible light. However, the signal-to-noise ratio still presents significant challenges. This study demonstrates an improved infrared target detection model for road infrared target detection. An advanced EfficientNet is incorporated to replace the conventional structure and enhance feature extraction. A Dilated-Residual U-Net is also introduced to reduce the noise of infrared images. Meanwhile, the k-means algorithm and data enhancement are implemented to improve the detection performance. The experimental results show that the mean average precision of the proposed model is observed to be 0.89 for the infrared road dataset with an average detection speed of 10.65 s −1 .

show abstract

Heat diffusion embedded level set evolution for infrared image segmentation

Cited by 2 publications

References 45 publications

Research on infrared target detection based on neural network fusion

Research on infrared target detection based on neural network fusion

Road infrared target detection with I‐YOLO

Contact Info

Product

Resources

About