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Object detection is an important branch of the computer vision field, and its essential content is: What objects are where? At the same time, as a basic task it supports some more complex problems in the field (such as instance segmentation, image text recognition, object tracking and so on). From the application point of view, object detection can be divided into two major categories: general object detection and detection applications. General object detection mainly explores methods for detecting different types of objects within a unified algorithmic framework, similar to human visual perception; detection applications focus on actual scenarios, such as pedestrian detection, face detection and text detection. ...

Usual object detection algorithms are all based on Anchors; although this generates many useless anchor boxes and brings a time-consuming NMS post-processing step, the accuracy of the algorithm is higher, because the anchor boxes basically cover all object regions. There is another approach in the detection field based on Anchor free, that is, without anchor boxes; as far as I know these are mainly Cornernet, Centernet (Keypoint Triplets for Object Detection) and Centernet (Objects as Points). This type of algorithm does not need to generate anchor boxes, and therefore does not need the time-consuming NMS post-processing. Here I mainly record the processing method of Centernet (Objects as Points). ...

Having studied so many deep learning algorithms, the loss function is always an unavoidable point, but fortunately the loss functions used in general are fairly fixed, with occasional changes made as things go. This post mainly lists some commonly used loss functions. L1-loss The L1 loss function, also known as Mean Absolute Error (MAE). Its mathematical expression is as follows: $$ MAE = \frac{1}{n}\sum^n_{i=1}\vert f(x_i)-y_i\vert $$where $n$ denotes the number of samples in the training batch, $f(x_i)$ denotes the value predicted by the model at sample $x_i$, and $y_i$ is the ground truth value corresponding to sample $x_i$. ...

At the end of 2019 and the beginning of 2020, Google Brain published two papers proposing a fast and accurate model for image classification and for object detection respectively, naming them EfficientNet and EfficientDet. The authors also adjusted the model parameters to obtain a series of efficient classification and detection models. EfficientNet From previous work and their own experiments, the authors summarized two observations: Scaling any single dimension of a model (depth, width, resolution) improves the model’s accuracy, but for very large scaling factors the accuracy gain diminishes In scaling a convolutional network, scaling all dimensions of the model in a balanced way yields better accuracy and speed compound scaling Based on the two observations above, the authors no longer use the traditional single-dimension model scaling but scale all dimensions of the model (depth, width and resolution). ...