A product defect detection method, comprising: Acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model comprises a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information, characterized in that the merging module comprises a concatenation layer and a merging convolutional layer, and in that the merging the plurality of first characteristic information by using the merging module, to obtain the second characteristic information comprises: performing channel concatenation on the plurality of first characteristic information by using the concatenation layer in the merging module, to obtain fourth characteristic information; and performing channel alignment on the fourth characteristic information by using the merging convolutional layer in the merging module, to obtain the second characteristic information with a standardized channel number. Acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model comprises a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information, characterized in that the merging module comprises a concatenation layer and a merging convolutional layer, and in that the merging the plurality of first characteristic information by using the merging module, to obtain the second characteristic information comprises: performing channel concatenation on the plurality of first characteristic information by using the concatenation layer in the merging module, to obtain fourth characteristic information; and performing channel alignment on the fourth characteristic information by using the merging convolutional layer in the merging module, to obtain the second characteristic information with a standardized channel number. performing channel concatenation on the plurality of first characteristic information by using the concatenation layer in the merging module, to obtain fourth characteristic information; and performing channel alignment on the fourth characteristic information by using the merging convolutional layer in the merging module, to obtain the second characteristic information with a standardized channel number.