Global-instance feature alignment domain adaptation detection method and system design

When actually applying the detection model, due to the difference between the real scene and the training data set, the effect of the detection algorithm is greatly affected. In order to obtain the better detection effect in the target scene, it is usually necessary to collect and label data and then train, which is not only costly but also complicated. The proposed global-instance domain adaptation detection algorithm and system based on the attention mechanism only needed to collect part of the real scene data to perform transfer learning, realizing rapid model training and remote deployment of edge-cloud integration. In this domain adaptation detection algorithm, the global feature adversarial learning algorithm based on the attention mechanism could reduce the negative effect of background features in transfer learning; the instance-level feature alignment method based on dictionary learning could align instance-level features with high precision. After experimental comparison, the proposed method reached a level close to SOTA(state-of-the-art), and the ablation experiment was quantitatively proved the improvement of the domain adaptation detection effect of this method. The proposed domain adaptation detection technology is combined with an edge system with data transmission links, improving the detection effect by nearly 10 points in actual scenarios.