This application provides a target detection method and device based on a neural network and training method and device of a neural network for target detection. The target detection method comprises: acquiring a to-be-detected image that contains a target;acquiring first feature information of the to-be-detected image by use of a first neural network, acquiring second feature information of the to-be-detected image by use of a second neural network;combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result by use of the second neural network and based on the combined feature information, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A target detection method, comprising: acquiring a to-be-detected image that contains a target; acquiring first feature information of the to-be-detected image by use of a first neural network that has been trained in advance, acquiring second feature information of the to-be-detected image by use of a second neural network that has been trained in advance; combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result by use of the second neural network and based on the combined feature information, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information. 2. The target detection method according to claim 1, further comprising: training the first neural network and the second neural network. 3. The target detection method according to claim 2, wherein training the first neural network and the second neural network comprises: adjusting a first network parameter of the first neural network by use of training images labeled with training targets to train the first neural network until a first loss function of the first neural network satisfies a first predetermined threshold condition, and obtaining the first neural network that is being trained; adjusting a second network parameter of the second neural network by use of the training images and training first feature information outputted with respect to the training images by the first neural network, to train the second neural network until a second loss function of the second neural network satisfies a second predetermined threshold condition, and obtaining the second neural network that is being trained. 4. The target detection method according to claim 3, wherein training the first neural network and the second neural network further comprises: repeating execution of the following training: adjusting the first network parameter by use of the training images and training second feature information outputted with respect to the training images by the second neural network that is being trained, to train the first neural network; and adjusting the second network parameter by use of the training images and the training first feature information outputted with respect to the training images by the first neural network that is being trained, to train the second neural network, until both the first loss function and the second loss function satisfy a third predetermined threshold condition, obtaining the trained first neural network and the trained second neural network. 5. The target detection method according to claim 3, wherein the first loss function indicates a difference between a prediction confidence of a pixel dot in the training images belongs to a training target as predicted by the first neural network and a true confidence thereof, and the second loss function indicates a difference between a prediction confidence of a target in the training images as predicted by the second neural network and a true confidence thereof, and a difference between a prediction confidence of a bounding box that contains the target and a true confidence thereof. 6. The target detection method according to claim 1, wherein the second neural network comprises: a feature input layer for acquiring the second feature information based on the to-be-detected image that is inputted; a first feature combination layer for combining the first feature information and the second feature information to acquire the combined feature information; and a target detection layer for acquiring a target detection result based on the combined feature information; and the first neural network comprises: a feature conversion layer for acquiring conversion feature information based on the second feature information; and a heat detection layer for acquiring the first feature information based on the conversion feature information. 7. The target detection method according to claim 6, wherein the first neural network further comprises an upsampling layer for upsampling the conversion feature information such that the heat detection layer receives upsampled conversion feature information with a size the same as a size of the to-be-detected image; the heat detection layer comprises: a second feature combination layer for receiving the upsampled conversion feature information to generate initial first feature information; a downsampling layer for downsampling the initial first feature information to obtain the first feature information with a dimension the same as a dimension of the second feature information; and a heat target prediction layer for obtaining a heat target prediction result based on the initial first feature information. 8. A training method of a neural network for target detection, the neural network for target detection comprising a first neural network and a second neural network, the training method comprising: adjusting a first network parameter of the first neural network by use of training images labeled with training targets to train the first neural network until a first loss function of the first neural network satisfies a first predetermined threshold condition, and obtaining the first neural network that is being trained; adjusting a second network parameter of the second neural network by use of the training images and training first feature information outputted with respect to the training images by the first neural network, to train the second neural network until a second loss function of the second neural network satisfies a second predetermined threshold condition, and obtaining the second neural network that is being trained. 9. The training method according to claim 8, further comprising: repeating execution of the following training: adjusting the first network parameter by use of the training images and training second feature information outputted with respect to the training images by the second neural network that is being trained, to train the first neural network; and adjusting the second network parameter by use of the training images and the training first feature information outputted with respect to the training images by the first neural network that is being trained, to train the second neural network, until both the first loss function and the second loss function satisfy a third predetermined threshold condition, obtaining the trained first neural network and the trained second neural network. 10. The training method according to claim 8, wherein the first loss function indicates a difference between a prediction confidence of a pixel dot in the training images belongs to a training target as predicted by the first neural network and a true confidence thereof, and the second loss function indicates a difference between a prediction confidence of a target in the training images as predicted by the second neural network and a true confidence thereof, and a difference between a prediction confidence of a bounding box that contains the target and a true confidence thereof. 11. The training method according to claim 8, wherein the first neural network is for acquiring first feature information of the to-be-detected image, the second neural network is for acquiring second feature information of the to-be-detected image; the second neural network further combines the first feature information and the second feature information to acquire combined feature information, and acquires a target detection result by use of the second neural network and based on the combined feature information, the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information. 12. The training method according to claim 11, wherein the second neural network comprises: a feature input layer for acquiring the second feature information based on the to-be-detected image that is inputted; a first feature combination layer for combining the first feature information and the second feature information to acquire the combined feature information; and a target detection layer for acquiring a target detection result based on the combined feature information; and the first neural network comprises: a feature conversion layer for acquiring conversion feature information based on the second feature information; and a heat detection layer for acquiring the first feature information based on the conversion feature information. 13. The training method according to claim 12, wherein the first neural network further comprises an upsampling layer for upsampling the conversion feature information such that the heat detection layer receives upsampled conversion feature information with a size the same as a size of the to-be-detected image; the heat detection layer comprises: a second feature combination layer for receiving the upsampled conversion feature information to generate initial first feature information; a downsampling layer for downsampling the initial first feature information to obtain the first feature information with a dimension the same as a dimension of the second feature information; and a heat target prediction layer for obtaining a heat target prediction result based on the initial first feature information. 14. A target detection device, comprising: a processor; a memory; and computer program instructions stored in the memory and configured to execute, when being run by the processor, steps of; acquiring a to-be-detected image that contains a target; acquiring first feature information of the to-be-detected image by use of a first neural network that has been trained in advance, acquiring second feature information of the to-be-detected image by use of a second neural network that has been trained in advance; combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result by use of the second neural network and based on the combined feature information, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information. 15. The target detection device according to claim 14, wherein a step of training the first neural network and the second neural network is executed when the computer program instructions are run by the processor: wherein the step of training the first neural network and the second neural network executed when the computer program instructions are run by the processor comprises: adjusting a first network parameter of the first neural network by use of training images labeled with training targets to train the first neural network until a first loss function of the first neural network satisfies a first predetermined threshold condition, and obtaining the first neural network that is being trained; adjusting a second network parameter of the second neural network by use of the training images and training first feature information outputted with respect to the training images by the first neural network, to train the second neural network until a second loss function of the second neural network satisfies a second predetermined threshold condition, and obtaining the second neural network that is being trained. 16. The target detection device according to claim 15, wherein the step of training the first neural network and the second neural network executed when the computer program instructions are run by the processor further comprises: repeating execution of the following training: adjusting the first network parameter by use of the training images and training second feature information outputtedwith respect to the training images by the second neural network that is being trained, to train the first neural network; and adjusting the second network parameter by use of the training images and the training first feature information outputted with respect to the training images by the first neural network that is being trained, to train the second neural network, until both the first loss function and the second loss function satisfy a third predetermined threshold condition, obtaining the trained first neural network and the trained second neural network, wherein the first loss function indicates a difference between a prediction confidence of a pixel dot in the training images belongs to a training target as predicted by the first neural network and a true confidence thereof, and the second loss function indicates a difference between a prediction confidence of a target in the training images as predicted by the second neural network and a true confidence thereof, and a difference between a prediction confidence of a bounding box that contains the target and a true confidence thereof. 17. The target detection device according to claim 14, wherein the second neural network comprises: a feature input layer for acquiring the second feature information based on the to-be-detected image that is inputted; a first feature combination layer for combining the first feature information and the second feature information to acquire the combined feature information; and a target detection layer for acquiring a target detection result based on the combined feature information; and the first neural network comprises: a feature conversion layer for acquiring conversion feature information based on the second feature information; and a heat detection layer for acquiring the first feature information based on the conversion feature information. 18. The target detection method according to claim 17, wherein the first neural network further comprises an upsampling layer for upsampling the conversion feature information such that the heat detection layer receives upsampled conversion feature information with a size the same as a size of the to-be-detected image; the heat detection layer comprises: a second feature combination layer for receiving the upsampled conversion feature information to generate initial first feature information; a downsampling layer for downsampling the initial first feature information to obtain the first feature information with a dimension the same as a dimension of the second feature information; and a heat target prediction layer for obtaining a heat target prediction result based on the initial first feature information.