Blur Object Detection via Simultaneous Compression and Segmentation
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Solution Overview
Problem
Traditional methods for detecting blur objects in images are time-consuming and resource-intensive, requiring image recovery and multiple processing steps before object detection can occur.
Innovation Solution
A blur object detection system and method that simultaneously performs image compression, blur object detection, and general object detection on dynamic images, allowing for the identification of blur objects without initial image recovery, thereby reducing computational resources and time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recovery and processing methods are used to detect blur objects, then object identification can be performed, but processing time and computing resource consumption increase significantly
Solution Approach 1:
The system performs preliminary classification of image regions to identify potential blur objects before applying computationally intensive recovery algorithms. By pre-segmenting the image and marking regions with blur characteristics, the system prepares data structures and identifies targets in advance, reducing the time required for subsequent detailed processing and object identification.
Solution Approach 2:
The image processing pipeline is segmented into distinct stages: initial blur detection, selective region identification, targeted recovery processing, and object identification. By dividing the processing into segments and applying different processing intensities to different regions (full recovery only for identified blur objects, simplified processing for general areas), the system reduces overall processing time while maintaining identification accuracy.
2Measurement precision
If traditional image recovery and processing methods are used to detect blur objects, then object identification can be performed, but computing resource consumption increases
Solution Approach 1:
The system applies different processing qualities and computational intensities to different regions of the image. Full image recovery and intensive processing are applied only to regions identified as containing blur objects, while other regions receive simplified or no recovery processing. This localized approach maintains object identification accuracy for blur objects while significantly reducing overall computing resource consumption.
Solution Approach 2:
Instead of performing complete image recovery on the entire image, the system performs partial recovery only on specific regions where blur objects are detected. This partial action approach applies computational resources selectively and proportionally to the actual need, avoiding the excessive resource consumption of full-image processing while maintaining sufficient accuracy for identification purposes.
3Measurement precision
If complete image recovery is performed before object detection, then accurate object identification can be achieved, but processing complexity increases
Solution Approach 1:
The system performs preliminary blur detection and region classification before initiating complex recovery processes. By identifying blur object regions in advance through simplified initial processing, the system prepares targeted processing masks and parameters, reducing the complexity of subsequent recovery operations. The preliminary action creates a roadmap that guides the complex processing only where necessary.
Solution Approach 2:
The processing pipeline is segmented into multiple stages with increasing complexity: simple blur detection, region selection, targeted recovery processing, and object identification. Each segment handles a specific task with appropriate complexity level, avoiding the need to apply maximum complexity throughout the entire processing chain. This segmentation reduces overall system complexity while maintaining detection accuracy.
Data Source
AI summary
A blur object detection system includes a memory, an image capturing device, and a processor. The image capturing device is configured to capture a dynamic image, in which the dynamic image includes a blur object and a general object. The processor is coupled to the image capturing device and the memory, and configured to simultaneously or non-simultaneously perform image compression, blur object detection, and general object detection based on the dynamic image to obtain an image compression file, a blur object position of the blur object in the dynamic image, and a general object position of the general object in the dynamic image, and store the image compression file, the blur object position, and the general object position to the memory.