Weakly Supervised Object Detection

Object detection is an important branch in the field of computer vision.It is a machine that uses vision to classify objects in real images and find the bounding box of the objects.It is widely used in intelligent monitoring,unmanned driving,image retrieval,etc.With the rapid development of convolutional neural networks in recent years,object detection algorithms based on deep learning have made long-term progress.However,the existing object detection algorithm usually requires a large amount of data with the location information of the object for training,and obtaining such data requires a great price and requires a lot of manpower.To this end,the researchers proposed an object detection algorithm in weakly supervised scenarios.In this case,data only with image-level tags are needed as supervision information,thus greatly reduces the difficulty of obtaining data.The algorithm of object detection based on weakly supervision has great practical significance.Therefore,it has attracted more and more researchers' attention and become a research hotspot in the field of computer vision.Although weakly supervised object detection is very attractive,it greatly increases the difficulty of algorithm training.The lack of position information of objects during training makes the algorithm difficult to recognize different types of targets in the image.In this research,the training of high-performance target detectors in weakly supervised scenarios is investigated.The object detection algorithms based on weakly supervised learning are reviewed.Most commonly used weakly supervised object detection models are based on multi-instance learning.A typical model of multi-instance learning is introduced in detail,and the impact of different back-end networks when applied to model is explored.The experimental results show that choosing the right back-end network has an importance to the model.Meanwhile,the current mainstream algorithms lack the ability to capture image information when extracting features.Hence,this thesis proposes an object detection algorithm based on the attention mechanism,which can effectively capture the global image information,facilitating to identify the object.Moreover,inspired by the object detection algorithm of supervised learning,after the training data with pseudo labels are obtained,the detection branch is introduced and the retraining of the bounding box to achieve end-to-end learning.Experiments show that the proposed algorithm based on the attention mechanism combined with the bounding box retraining,improves the performance of object detection in weakly supervised scenes.