Trailer Coupler Detection Using Rear Camera and Neural Networks

The system enables the vehicle to autonomously detect and align with the trailer coupler using rear-facing cameras and convolutional neural networks. The vehicle performs self-guidance by automatically processing visual data to determine coupler position and distance, eliminating the need for external assistants or manual trial-and-error adjustments.

The patent replaces manual mechanical alignment operations with an automated visual recognition system. Instead of relying on human operators to visually estimate distances and positions, the system uses image processing algorithms to automatically detect the coupler and calculate alignment parameters, substituting mechanical human judgment with computational analysis.

The detection system is divided into multiple specialized convolutional neural networks, each trained to detect couplers at specific distance ranges. By segmenting the detection task into distance-specific modules, the system achieves high precision for each range while keeping individual network complexity manageable. Each network focuses on a specific spatial zone, improving overall accuracy without requiring a single overly complex model.

Multiple convolutional neural networks are deployed that can collectively handle various detection scenarios. The system uses a family of networks that work together to provide universal coupler detection capability across different distances and lighting conditions, where each network serves a specific function but collectively they provide comprehensive coverage.