NIGHT SHIPPING IDENTIFICATION BASED ON RESIDUAL NETWORK AND ATTENTION MECHANISM

Aimed at the poor performance of conventional deep learning models in identifying night surveillance videos, a method for identifying night shipping events based on residual network and attention mechanism is proposed. The illumination in the dark image generated by the night surveillance video was enhanced, and the SE-R2 (2+1) model was used to identify the video combined by the enhanced image. The recognition model was based on the R(2+1)D model. By improving the activation structure of the model, the generalization ability of the model was improved. At the same time, the SENet network was embedded to improve the characterization ability of the model. Experimental results show that under the enhanced dataset, the recognition accuracy of the proposed method reaches 88.2%, which verifies the effectiveness of the model.