Construction and Application Research of the Visual Image Obstacle Type Recognition Model Based on the...

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Traditional obstacle recognition methods struggle with environmental factors, limiting their use in complex scenarios.
• Existing techniques lack accuracy, reliability, and universality for intelligent and unmanned vehicles.
• High costs associated with traditional detection equipment pose a barrier to widespread adoption.

Purpose of the Study:

• To develop a robust obstacle detection and type recognition system for intelligent vehicles.
• To enhance recognition accuracy, reliability, and generalization capabilities.
• To address the limitations of traditional methods and high equipment costs.

Main Methods:

• Utilizing a dilated convolutional neural network (CNN) for autonomous feature learning directly from input images.
• Applying a hierarchical CNN structure with weight sharing to learn obstacle characteristics and extract global features.
• Integrating the Region of Interest (ROI) algorithm for real-time detection and precise recognition.

Main Results:

• The dilated CNN approach achieves high recognition accuracy without cumbersome preprocessing.
• The system demonstrates improved reliability and generalization across various environmental conditions.
• Integration with the ROI algorithm enables real-time obstacle detection and high-precision type recognition.

Conclusions:

• Dilated CNNs offer a superior alternative to traditional methods for obstacle detection and recognition.
• The proposed system meets the technical requirements for intelligent and unmanned vehicles in complex environments.
• This approach provides a cost-effective and highly accurate solution for advanced obstacle recognition.