A computer-implemented method for quantizing an activation volume of a deep neural network, wherein the deep neural network is configured for target detection and segmentation, and the activation volume is data flow transmitted between layers in the deep neural network when running the deep neural network, and wherein the method comprises: obtaining an activation volume of a network layer in the deep neural network (S101), wherein elements in the activation volume are arranged in three directions: a height direction, a width direction, and a depth direction; dividing depth segments in the activation volume in which a difference among element features is smaller than a preset threshold into one slice group along the depth direction of the activation volume, so as to obtain a plurality of slice groups (S102); and quantizing each slice group respectively by using a quantization parameter corresponding to each slice group obtained by using a quantization formula (S103); wherein the quantization parameter comprises a quantization step, and wherein different quantization steps are used for different slice groups, wherein quantizing each slice group respectively by using a quantization parameter corresponding to each slice group obtained by using a quantization formula comprises: obtaining the quantization step of each slice group by using the quantization formula according to a preset maximum quantization value and a preset number of bits corresponding to each slice group, wherein the quantization formula is: step = A 2 B − 1 wherein, step is the quantization step, A is the preset maximum quantization value, and B is the preset number of bits; quantizing each slice group respectively by using the quantization step of each slice group, and performing target detection and segmentation using each quantized slice group. obtaining an activation volume of a network layer in the deep neural network (S101), wherein elements in the activation volume are arranged in three directions: a height direction, a width direction, and a depth direction; dividing depth segments in the activation volume in which a difference among element features is smaller than a preset threshold into one slice group along the depth direction of the activation volume, so as to obtain a plurality of slice groups (S102); and quantizing each slice group respectively by using a quantization parameter corresponding to each slice group obtained by using a quantization formula (S103); wherein the quantization parameter comprises a quantization step, and wherein different quantization steps are used for different slice groups, wherein quantizing each slice group respectively by using a quantization parameter corresponding to each slice group obtained by using a quantization formula comprises: obtaining the quantization step of each slice group by using the quantization formula according to a preset maximum quantization value and a preset number of bits corresponding to each slice group, wherein the quantization formula is: step = A 2 B − 1 wherein, step is the quantization step, A is the preset maximum quantization value, and B is the preset number of bits; quantizing each slice group respectively by using the quantization step of each slice group, and performing target detection and segmentation using each quantized slice group. obtaining the quantization step of each slice group by using the quantization formula according to a preset maximum quantization value and a preset number of bits corresponding to each slice group, wherein the quantization formula is: step = A 2 B − 1 wherein, step is the quantization step, A is the preset maximum quantization value, and B is the preset number of bits; quantizing each slice group respectively by using the quantization step of each slice group, and performing target detection and segmentation using each quantized slice group. <EOS>