IoU is an important metric in object detection; it measures how good a predicted box is by computing the ratio of the intersection to the union of the predicted box and the ground truth box. But the usual bounding box adjustment function generally adopts the L2 norm, that is, the loss is computed in an MSE manner, and some studies have shown that this is not the optimal optimization function, hence the IOU loss functions. ...
Papers keep piling up before I know it, and it is quite exhausting. Back to the point: neural networks are already applied in many fields and even surpass human ability in image recognition, but the improvement in model accuracy comes at the cost of higher computing requirements. Real engineering applications often cannot provide much computing power, and some algorithms have to run on embedded platforms, which means those fancy algorithms simply cannot be deployed. Following mobilenetv1, Google proposed its improved version mobilenetv2, which is better suited to lightweight deployment and can keep a decent accuracy while maintaining running speed. ...
Most current object detection/segmentation models are based on Fast/Faster RCNN and FPN; the authors mainly provide a comparable framework for instance segmentation, namely Mask R-CNN. Introduction mask rcnn works by adding a branch on top of faster rcnn to predict segmentation masks in parallel. This branch is in fact a small FCN network applied to each ROI region. A brief note on fast/faster rcnn here: fast rcnn introduced ROIPool on top of rcnn, and faster rcnn introduced RPN on top of fast rcnn. Instance segmentation delineates object contours at the pixel level. There are two commonly used strategies: ...
Yes, it has arrived. Introduction With a $320\times 320$ resolution input, yolo3 keeps its accuracy at $28.2mAP$ and runs in roughly $22ms$. Compared with yolo2, this model does not bring any major changes; the authors tried many tricks to improve model performance, and finally selected the ones that proved useful and combined them into yolo3. Bounding box and class prediction In yolo2, bounding boxes are obtained by predicting the center coordinates of the box relative to the top-left corner of the image and the width and height relative to the anchors: ...