Blind Spot Detection System in Vehicles Using Fusion of Radar Detections and Camera Verification

Bagi, Shayan Shirahmad Gale; Moshiri, Behzad; Garakani, Hossein Gharaee; Khoshnevisan, Mohammad

doi:10.1007/s13177-021-00254-5

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…Information fusion can provide better, more accurate detection through the complementary advantages of sensors [22]. Compared to fusion schemes such as LIDAR and vision fusion, MWR and vision sensor fusion have lower hardware costs and better detection robustness.…”

Section: Vehicle Detection Based On Millimeter-wave Radar and Vision ...mentioning

confidence: 99%

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Multi-sensor information fusion algorithm for night driving vehicle detection

Chen,

Shi,

Luo

et al. 2024

Preprint

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The ability to effectively detect vehicles and obtain their comprehensive information through sensors to achieve early warning of a collision and intelligent long-range light exposure are the basic prerequisites for safe night driving. However, sensors have their own performance characteristics, and a single type of sensor cannot fully perceive the environment. Therefore, a night driving vehicle-detection algorithm based on information fusion is proposed. An effective vehicle target priming algorithm based on millimeter-wave radar is designed that is experimentally shown to filter out a large number of irrelevant targets. A YOLOv5 benchmark model is optimized as follows: (a) intelligent data resampling addresses category imbalance; (b) head shared convolution addresses scale imbalance; and (c) lossless mosaic data enhancement solves the inconsistency between target features and labels generated by the original mosaic. A target matching algorithm correlates the detection results of a single sensor, providing comprehensive and reliable information including relative position and speed, category, and far light shield angle, laying a solid foundation for night driving safety.

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Section: Vehicle Detection Based On Millimeter-wave Radar and Vision ...mentioning

confidence: 99%

“…The method is reliable and accurate in low-visibility environments such as fog or dust[28]. In 2021, Bagi S et al proposed a blind spot detection method for vehicles based on the fusion of MWR and vision sensors, improving them as through data correlation[22].…”

mentioning

confidence: 99%

Multi-sensor information fusion algorithm for night driving vehicle detection

Chen,

Shi,

Luo

et al. 2024

Preprint

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“…A common practice is to fuse the millimeter wave radar data into an object recognition model based camera [3][4] . Target layer fusion matches and associates [5][6] the target parameters which obtained through processing each sensor data individually before, it is widely used because of good compatibility. Although all these different fusion methods can achieve better perception effects than single sensors, in the condition of extreme weather (such as hazy environment) where camera data is highly disturbed, a significant decrease in obstacle classification accuracy is resulted from that the perception results of current fusion methods are often more dependent on millimeter wave radar.…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion-based obstacle detection method for hazy environment

Wang

et al. 2023

Third International Conference on Sensors and Information Technology (ICSI 2023)

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Based on camera and millimeter wave radar data fusion, an intelligent vehicle obstacle detection method suitable for hazy environment is proposed. Firstly, by taking the effectiveness of obstacle detection after image dehazing as the evaluation standard, a series of typical dehazing networks are compared and the best one was selected for image preprocessing. An obstacle detection model based on YOLOv5s depth network was established; Then, the camera data and radar data are fused in time and space, and the sensor data is associated based on the global nearest neighbor data association algorithm. Finally, the effectiveness of the proposed method is verified by open source data sets and real vehicle experiments.

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Sensor Networks as a Support Mechanism in Intelligent Transportation Systems

Pop,

Ramasamy

2024

Studies in Systems, Decision and Control

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Blind Spot Detection System in Vehicles Using Fusion of Radar Detections and Camera Verification

Cited by 6 publications

References 16 publications

Multi-sensor information fusion algorithm for night driving vehicle detection

Multi-sensor information fusion algorithm for night driving vehicle detection

Sensor fusion-based obstacle detection method for hazy environment

Sensor Networks as a Support Mechanism in Intelligent Transportation Systems

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