Method and apparatus for neural network training and construction and method and apparatus for object detection

A training method of a neural network for object detection, comprising:

• inputting a training image including a training object to the neural network to obtain a predicted bounding box of the training object;
• acquiring a first loss function according to a ratio of an intersection area to a union area of the predicted bounding box and a true bounding box, wherein the true bounding box is a bounding box of the training object marked in advance in the training image;
• and
• adjusting parameters of the neural network by utilizing at least the first loss function to train the neural network;
• wherein the first loss function is a negative value of a natural logarithm of the ratio of the intersection area to the union area of the predicted bounding box and the true bounding box;
• wherein the method further comprising:
• selecting a second loss function reflecting a difference between a predicted confidence and a true confidence of each pixel point in the training image, the predicted confidence being the confidence, predicted by employing the neural network, that a certain pixel point in the training image belongs to the training object, and the true confidence representing the confidence, marked in advance in the training image, that the certain pixel point belongs to the training object,
• wherein said adjusting the parameters of the neural network by utilizing at least the first loss function to train the neural network comprises:
• adjusting the parameters of the neural network by utilizing the first loss function and the second loss function to maximize a ratio of an intersection area to an union area of the predicted bounding box and the true bounding box and to minimize the second loss function so as to obtain the trained neural network.