An Anchor-free Object Detection Model Based on EfficientNet
Object detection is one of the hot research areas in computer vision,which includes two tasks:classification and location.Due to the two common problems appearing in one-stage object detector:extreme imbalance between positive/negative samples during training and anchors pre-defined deeply depending on manual settings,an anchor-free efficientnet-based object detector(AEOD)is pro-posed.AEOD first selects out the feature points that fall in the target box,then calculates the cost matrix based on values predicted by these feature points,finally assigns the positive/negative samples to the target dynamically according to the cost matrix during the training.Therefore,the number of positive/negative samples is balanced to enhance the performance of the model.AEOD directly predicts location and shape of the object through the feature points in the feature maps.As a result,not only the step of pre-defining anchors can be skipped,but also the number of objects that successfully detected increases.In addition,the scalable backbone(EfficientNet)improves the generalization ability of AEOD,it can receive multi-scale input.AEOD achieves the highest 91.3%mAP on PASCAL VOC07+12 at speed of 32.1 FPS,showing a significant improvement compared to other modern models.
deep learningcomputer visionobject detectionpositive/negative samples assignment algorithmanchor-free
国家科技期刊平台
NSTL
万方数据
维普
