Abstract: The need for large scale data in deep learning and the complexity of object detection annotation task promote the deve-lopment of semi-supervised object detection.In recent years,semi-supervised object detection has achieved many excellent results.However,the uncertainty in pseudo labels is still an unavoidable problem in semi-supervised object detection.The superior semi-supervised method requires an appropriate filtering threshold to balance the proportion of pseudo labels' noise and the recall rate,so as to retain accurate and effective labels as much as possible.To solve this problem,this paper introduces a sequential three-way decision algorithm into semi-supervised object detection,which divides the model output pseudo-labels into clean foreground labels,noisy foreground labels,and clean background labels according to different filtering thresholds,and adopts different processing strategies for them.For noisy foreground labels,we use negative class learning loss to learn these noisy labels,thereby avoiding learning noise information from them.Experimental results show the performance advantage of this algorithm.For COCO dataset,this method achieves performance of 35.2% when supervised data only accounts for 10%,which outperforms the supervised results by 11.34%.

Key words: Sequential three-way decisions, Uncertaint, Negative class learning, Semi-supervised learning, Semi-supervised object detection