Aiming at the problem of low accuracy of impurity detection in raw cotton,an improved algorithm based on residual and attention mechanism for detecting raw cotton impurity in Xinjiang cotton is proposed.The algorithm has high accuracy and is a two-stage algorithm.Firstly,the impurity images of raw cotton were collected and labeled,and then the data were enlarged to avoid the overfitting phenomenon in the training process.Then,visual attention mechanism is introduced into the original framework,and the accuracy of impurity detection of raw cotton is improved by advancing the algorithm structure.Secondly,by analyzing and comparing the accuracy of several different networks in detecting raw cotton impurities,ResNet50 was selected as the feature extraction network,which improved the complex feature extraction ability of the algorithm.Finally,ROI Align is used to reduce quantization errors and improve the accuracy of the detection of raw cotton impurities.Experimental results show that although the improved algorithm slightly increases the detection time,its overall detection accuracy is significantly better than the original algorithm,and the overall recognition accuracy can reach about 94.84%,which is 5.58% higher than the recognition rate of the faster region-based convolutional neural network (Faster R-CNN) before the improvement.Meanwhile,by comparing different network models,the results show that the improved Faster R-CNN has a better effect on the detection of raw cotton impurities.