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.