A defect detection method to be carried out by a defect detection apparatus, the method comprising: obtaining an image for inspection; performing anomaly detection on the image for inspection to obtain an anomaly region image corresponding to the image for inspection; and performing defect classification on the anomaly region image to obtain defect detection information of the image for inspection the performing anomaly detection on the image for inspection to obtain an anomaly region image corresponding to the image for inspection comprises: obtaining, based on an anomaly detection model, a difference image used to represent an anomaly region; and segmenting the difference image based on a segmentation threshold to obtain the anomaly region image, characterized in that the anomaly detection model comprises a pre-trained teacher network and a student network distilled from the teacher network; and the obtaining, based on an anomaly detection model, a difference image used to represent an anomaly region comprises: inputting the image for inspection into both the teacher network and the student network so as to obtain a feature image output by the teacher network and a feature image output by the student network; and calculating a difference between each pixel of the feature image output by the teacher network and each pixel of the feature image output by the student network to obtain the difference image; wherein the method further comprises calculating an optimal anomaly determining threshold of the anomaly detection model and setting the segmentation threshold to be less than the optimal anomaly determining threshold. obtaining an image for inspection; performing anomaly detection on the image for inspection to obtain an anomaly region image corresponding to the image for inspection; and performing defect classification on the anomaly region image to obtain defect detection information of the image for inspection the performing anomaly detection on the image for inspection to obtain an anomaly region image corresponding to the image for inspection comprises: obtaining, based on an anomaly detection model, a difference image used to represent an anomaly region; and segmenting the difference image based on a segmentation threshold to obtain the anomaly region image, characterized in that the anomaly detection model comprises a pre-trained teacher network and a student network distilled from the teacher network; and the obtaining, based on an anomaly detection model, a difference image used to represent an anomaly region comprises: inputting the image for inspection into both the teacher network and the student network so as to obtain a feature image output by the teacher network and a feature image output by the student network; and calculating a difference between each pixel of the feature image output by the teacher network and each pixel of the feature image output by the student network to obtain the difference image; wherein the method further comprises calculating an optimal anomaly determining threshold of the anomaly detection model and setting the segmentation threshold to be less than the optimal anomaly determining threshold. obtaining, based on an anomaly detection model, a difference image used to represent an anomaly region; and segmenting the difference image based on a segmentation threshold to obtain the anomaly region image, characterized in that the anomaly detection model comprises a pre-trained teacher network and a student network distilled from the teacher network; and the obtaining, based on an anomaly detection model, a difference image used to represent an anomaly region comprises: inputting the image for inspection into both the teacher network and the student network so as to obtain a feature image output by the teacher network and a feature image output by the student network; and calculating a difference between each pixel of the feature image output by the teacher network and each pixel of the feature image output by the student network to obtain the difference image; wherein the method further comprises calculating an optimal anomaly determining threshold of the anomaly detection model and setting the segmentation threshold to be less than the optimal anomaly determining threshold.