YOLOv5 Panoptic Driving Perception for Real-Time Multi-Task Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing panoptic driving perception algorithms for autonomous vehicles face challenges in achieving real-time and high-precision traffic target detection, drivable area segmentation, and lane line detection due to the use of separate branch networks for each task, which increases reasoning time.
Innovation Solution
A multi-task panoptic driving perception method based on improved YOLOv5, utilizing an inverted residual bottleneck module in the backbone network and a branch network with convolutional and BottleneckCSP layers, along with a feature pyramid network and spatial pyramid pooling, to fuse and process these tasks simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate branch networks are used for traffic target detection, drivable area segmentation, and lane line detection, then each task can be processed independently with dedicated optimization, but the reasoning time of the network increases
Solution Approach 1:
The patent merges three separate branch networks into a unified network architecture based on YOLOv5. The detector head simultaneously performs traffic target detection, drivable area segmentation, and lane line detection by sharing the backbone feature extraction and using a unified detection mechanism, thereby reducing reasoning time while maintaining detection precision through multi-task learning
Solution Approach 2:
The detector head is designed with multi-functionality to handle multiple detection tasks simultaneously. It uses a unified detection mechanism that can process traffic targets, drivable areas, and lane lines through the same network path, making the system more efficient without sacrificing the specialized capabilities needed for each task type
2Productivity
If multiple tasks are processed simultaneously using a multi-task network, then image analysis speed is accelerated and information is shared among tasks, but the network complexity increases
Solution Approach 1:
The network is segmented into distinct functional modules: backbone for feature extraction, neck for feature fusion, and detector head for multi-task detection. This modular segmentation allows simultaneous processing of multiple tasks while managing complexity through clear separation of concerns and reusable components
Solution Approach 2:
The network employs dynamic feature fusion mechanisms in the neck module that adaptively combine features from different levels and branches. This dynamic approach allows the network to flexibly allocate computational resources across different tasks based on their specific needs, improving analysis speed while controlling overall complexity
3Speed
If YOLOv5 backbone network is used for feature extraction, then detection speed is improved, but the network may lack sufficient feature representation for complex panoptic tasks
Solution Approach 1:
The backbone network performs preliminary feature extraction at multiple scales and levels before the neck module refines these features for specific tasks. This preliminary action captures general patterns quickly while leaving room for task-specific refinement, maintaining speed while improving feature representation accuracy for complex panoptic tasks
Data Source
AI summary
The present disclosure provides a multi-task panoptic driving perception method and system based on improved You Only Look Once version 5 (YOLOv5). The method in the present disclosure includes: performing image preprocessing on an image in a dataset to obtain an input image; extracting a feature of the input image by using a backbone network of improved YOLOv5, to obtain a feature map, where the backbone network is obtained by replacing a C3 module in a backbone network of YOLOv5 with an inverted residual bottleneck module; inputting the feature map into a neck network to obtain a feature map, and fusing the obtained feature map and the feature map obtained by the backbone network; inputting the fused feature map into a detection head to perform traffic target detection; and inputting the feature map of the neck network into a branch network to perform lane line detection and drivable area segmentation.