A method for detecting target points in an image, the method comprising: obtaining an image under test, wherein the image under test comprises a structure-stable first target object and a structure-unstable second target object, wherein the image under test is an image depicting an adapting piece and a pole that are welded in a battery cell, the first target object is the pole, and the second target object is the adapting piece; and processing the image under test based on a target point detection model to obtain the target points in the image under test, wherein the target point detection model is a neural network, and the target points comprise a feature point on the structure-stable first target object and a feature point on the structure-unstable second target object, training parameters of the target point detection model based on a sample image and labeled data, wherein the sample image comprises the first target object and the second target object, and the labeled data comprises a labeled feature point on the first target object and a labeled feature point on the second target object; characterized in that the method further comprises: before the training a parameter of the target point detection model based on a sample image and labeled data, obtaining the labeled data based on the sample image and an initial target point detection model, wherein the initial target point detection model is a further neural network, wherein the obtaining the labeled data based on the sample image and an initial target point detection model comprises: inputting the sample image into the initial target point detection model to obtain an initial predicted target point of the sample image; determining an initial loss value based on the initial predicted target point and initial labeled data; and adjusting the initial labeled data to make the initial loss value satisfy a second preset loss value, so as to obtain the labeled data. obtaining an image under test, wherein the image under test comprises a structure-stable first target object and a structure-unstable second target object, wherein the image under test is an image depicting an adapting piece and a pole that are welded in a battery cell, the first target object is the pole, and the second target object is the adapting piece; and processing the image under test based on a target point detection model to obtain the target points in the image under test, wherein the target point detection model is a neural network, and the target points comprise a feature point on the structure-stable first target object and a feature point on the structure-unstable second target object, training parameters of the target point detection model based on a sample image and labeled data, wherein the sample image comprises the first target object and the second target object, and the labeled data comprises a labeled feature point on the first target object and a labeled feature point on the second target object; characterized in that the method further comprises: before the training a parameter of the target point detection model based on a sample image and labeled data, obtaining the labeled data based on the sample image and an initial target point detection model, wherein the initial target point detection model is a further neural network, wherein the obtaining the labeled data based on the sample image and an initial target point detection model comprises: inputting the sample image into the initial target point detection model to obtain an initial predicted target point of the sample image; determining an initial loss value based on the initial predicted target point and initial labeled data; and adjusting the initial labeled data to make the initial loss value satisfy a second preset loss value, so as to obtain the labeled data. before the training a parameter of the target point detection model based on a sample image and labeled data, obtaining the labeled data based on the sample image and an initial target point detection model, wherein the initial target point detection model is a further neural network, wherein the obtaining the labeled data based on the sample image and an initial target point detection model comprises: inputting the sample image into the initial target point detection model to obtain an initial predicted target point of the sample image; determining an initial loss value based on the initial predicted target point and initial labeled data; and adjusting the initial labeled data to make the initial loss value satisfy a second preset loss value, so as to obtain the labeled data. inputting the sample image into the initial target point detection model to obtain an initial predicted target point of the sample image; determining an initial loss value based on the initial predicted target point and initial labeled data; and adjusting the initial labeled data to make the initial loss value satisfy a second preset loss value, so as to obtain the labeled data.