An improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes

The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 27; číslo 24; s. 18585 - 18604
Hlavní autor: Zhang, Wenying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Autonomous cars
Clustering
Computational Intelligence
Control
Cost analysis
Data processing
Decision making
Density distribution
Design
Efficiency
Embedded systems
Engineering
Hazard identification
Heat detection
Identification systems
Internet of Things
Lasers
Mathematical Logic and Foundations
Mechatronics
Obstacle avoidance
Optimization
Perception
Power
Recognition
Robotics
Sensors
Smoothing
Traffic accidents & safety
Trains
Unmanned vehicles
Vehicles
AI image processing
Unmanned
Obstacles
Bounding box
Hazard identification
DBSCAN Algorithm
ISSN:1432-7643, 1433-7479
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Shrnutí:The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional DBSCAN by refining the core point definition using an adaptive density threshold. It identifies the “elbow point” in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-09319-x