Weakly Supervised Object Detection Method And Application Under Image Level Annotation

Object detection is not only one of the most basic tasks in the field of computer vision,but also an indispensable technology for many advanced applications,such as autonomous driving,face recognition,image retrieval,etc.Benefit from accurate instance-level data annotation,traditional fully-supervised object detection algorithms can achieve extremely high accuracy.However,in actual situations,data annotation is not only expensive to obtain,but also extremely difficult to ensure accuracy.Therefore,researchers began to turn their attention to the research of weakly-supervised object detection algorithms under image-level supervision.In recent years,a basic framework based on multiple instance learning has been formed for weakly-supervised object detection task.On this basis,researchers try to obtain better detection results by optimizing the generation of proposals,adding self-training modules and other methods.However,the existing algorithms are still difficult to properly handle the three major problems in this field(instance ambiguity,part domination and memory consumption),which makes these algorithms unable to be applied to practical tasks.Therefore,this paper focuses on researching more robust weakly-supervised object detection algorithms under image-level supervision and trying to actually apply.The main work includes the following two aspects:1)A weakly-supervised object detection algorithm based on class activation map refinement is proposed.The proposed algorithm can leverage the weakly-supervised semantic segmentation branch to refine the Class Activation Maps(CAMs)generated by the weaklysupervised object detection branch.Then the refined CAMs are utilized to provide more reliable foreground localization cues for the weakly-supervised object detection branch in turn.By such iterative optimizations,better results can be achieved on both tasks.Further,on some public datasets,ablation experiments have been conducted to verify the rationality and effectiveness of the mutual guidance between the weakly-supervised semantic segmentation branch and the weakly-supervised object detection branch in the proposed algorithm.Then the proposed algorithm is compared with existing weakly-supervised object detection algorithms on some difficult samples and some evaluation indicators,which proves the superiority of the algorithm proposed in this paper.2)In order to apply the weakly-supervised object detection algorithm proposed in this paper to the major needs of CSAC,an application software is designed and implemented,which automates the entire process from data preparation to model training to inference.The software can make full use of the algorithm proposed in this paper to help researchers reduce the burden of labeling,improve the detection results through more data,and provide low-cost solutions for some practical problems in aerospace research such as satellite environment recognition,satellite attitude adjustment,and satellite device maintenance in the space environment.The software is implemented based on the flask framework and deployed using docker image,which has relatively high portability and scalability.