Abstract: 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.

Key words:  holistic artificial intelligence ,  computer vision ,   neural network ,  object detection