Multi-Camera Soiling Detection Using Neural Image Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for detecting lens soiling on vehicle-mounted image capture devices are inadequate, as they fail to effectively utilize multiple image sources and environmental conditions to enhance decision-making and accuracy in soiling detection.
Innovation Solution
A neural network image processing apparatus utilizing convolutional neural networks (CNNs) processes images from multiple image capturing devices with different fields of view, incorporating sensor data to enhance soiling detection and classification, allowing for end-to-end training and synergized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single image capturing device is used for soiling detection, then the device complexity is reduced, but the measurement precision of soiling detection deteriorates
Solution Approach 1:
The patent combines multiple image capturing devices with different fields of view into a unified soiling detection system. The neural network processor integrates images from multiple devices to comprehensively assess soiling conditions, thereby improving detection accuracy while managing system complexity through centralized processing.
Solution Approach 2:
The patent introduces a new dimension of detection by using multiple image capturing devices with different fields of view (front, rear, side cameras) rather than relying on a single device. This multi-dimensional approach allows the system to detect soiling from various angles and perspectives, significantly enhancing detection precision.
2Measurement precision
If multiple image capturing devices are used for soiling detection, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The neural network processor is designed with multi-functionality to handle various image processing tasks simultaneously - soiling detection, object detection, and semantic segmentation. This universal processor manages multiple image capturing devices and performs diverse functions, thereby improving detection precision without proportionally increasing overall system complexity.
Solution Approach 2:
The neural network processor acts as an intermediary that receives and integrates data from multiple image capturing devices. It processes and synthesizes information from different sources to produce accurate soiling detection results, mediating between the complexity of multiple sensors and the need for precise measurement.
3Reliability
If soiling detection is performed before object detection and semantic segmentation, then the reliability of vehicle systems is improved, but the loss of time in processing increases
Solution Approach 1:
The system performs soiling detection as a preliminary action before object detection and semantic segmentation. By detecting soiling conditions first, the system can determine whether subsequent processing is necessary, thereby improving reliability while potentially reducing overall processing time by avoiding unnecessary computations when soiling is present.
Solution Approach 2:
The processing workflow is made dynamic based on soiling detection results. When soiling is detected, the system adjusts its processing sequence and priorities, dynamically allocating computational resources to maintain reliability while minimizing processing time losses.
Data Source
Figure 1
Figure 2
AI summary
A neural network image processing apparatus obtains a first image and a respective additional image, wherein a first image capturing device has a field of view different than each additional image capturing device. Each image is processed through respective instances of a common feature extraction processing network to produce a respective first map and at least one additional map. Feature classification comprises processing the first map to provide a first classification indicating a soiling of the first image; processing the or each additional map to provide at least one additional classification indicating a respective soiling of the or each additional image; and combining the first and the or each additional classification to provide a boosted classification indicating soiling of the first image. If the boosted classification doesn't indicate soiling of the first image capturing device, further processing can be performed.