Cross-Domain Object Detection Algorithm for Complex End-to-End Scene Understanding
Conventional deep learning training approaches often assume a similarity between the deployment scenario and the visual domain features present in the training data.However,this assumption might not hold true in complex end-to-end scenarios,making it difficult to meet the demands of intelligent detection services in open environments.In response,an object detection algorithm based on artificial intelligence closed-loop ensemble theory with cross-domain capabilities has been introduced.Within the detection framework,construct a backbone network and bottleneck layer network with multi-scale convolutional layers.A visual domain discriminator featuring long-range dependency attention works as a secondary detection head to refine the results.Moreover,a background focusing module,based on spatial reconstruction attention units,is able to enhance learning focused on pseudo-background representations,thereby improving the accuracy of cross-domain object detection.Experimental results show that,compared to two-stage algorithms,the proposed algorithm yields an average precision increase 6.9％,and surpasses single-stage algorithms by 9.0％ in complex end-to-end scenarios.
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