A residual attribute prompt driven detection and incremental learning method for unknown thrown objects

By using residual attribute hints to drive detection and incremental learning, the problem of identifying unknown spilled objects is solved, achieving pixel-level segmentation and real-time hierarchical early warning. It is suitable for deployment on edge devices, reduces resource consumption, and adapts to changes in lighting and weather.

CN122597770APending Publication Date: 2026-08-18NANJING UNIV OF SCI & TECH
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Patent Information

Application Number
CN202610764878.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-29
Publication Date
2026-08-18

AI Technical Summary

Technical Problem

Existing deep learning-based object detection methods struggle to identify unknown types of spilled objects in road inspection and intelligent transportation scenarios, and suffer from issues such as missed detections, false detections, and catastrophic forgetting. They also cannot achieve pixel-level segmentation and geometric constraints, have high resource consumption, and lack real-time performance.

Method used

We employ a residual attribute hint-driven detection and incremental learning method. Candidate boxes are generated through contextual residuals, and semantic judgment and pixel-level segmentation are performed by combining visual-language alignment. By introducing a residual attribute hint generation mechanism, we achieve online closed-loop learning, which is suitable for deployment on edge devices.

Benefits of technology

It effectively identifies unknown spills, reduces missed and false detections, lowers resource consumption, enables real-time hierarchical early warning and model updates, adapts to changes in lighting and weather, and supports cross-platform deployment.

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Abstract

The application discloses a residual attribute prompt driven detection and incremental learning method for unknown scattering objects, aiming to solve the problems of missing detection, false detection and lack of self-learning ability of existing inspection on training set outside scattering objects. When the inspection vehicle or unmanned aerial vehicle enters the working area, start the visible light camera to collect real-time video stream; use the context residual learning module to detect the abnormal area of the video image, generate the residual energy graph to locate the potential unknown scattering object; based on the shape, texture and reflectivity statistics of the candidate frame area, construct the attribute vector and automatically generate the descriptive semantic prompt word, the prompt and the preset word library are fused through the gate weight to drive the open visual-linguistic segmentation, output the boundary information and semantic label of the scattering object; through the adaptive incremental learning module, the new detection sample is updated and knowledge playback with few samples, realizing the dynamic expansion and continuous evolution of the knowledge base. The application realizes the discovery of unknown targets through residual detection, completes semantic recognition and boundary extraction through open segmentation, and continuously expands the detection category through adaptive incremental learning, constructs a 'discovery-recognition-learning' closed loop mechanism, thereby significantly improving the processing ability of road inspection on unknown scattering objects in complex environment.
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