Crop seeds sorting is an important task for industrial production. In the sunflower seed production processing, a large number of impurities are mixed into the seeds. Besides, the image of sunflower seeds has the characteristics of random distribution, various kinds of bad sunflower seeds. In addition, the distinction between the bad seeds and normal seeds is not obvious. the recognition rate of traditional methods suffer from a low accuracy is not meeting the actual requirements. In the past few years, many methods based on convolutional neural network (CNN) has made great success in object detection and recognition. However, these networks have a large model size. In this paper, we developed a CNN model with eight convolutional layers to extract the image feature and a skip connection is used to increase the learning ability of the model. Compared with the classical convolution network, it has smaller size without reducing the accuracy. However, because of the CNN features obtained by the convolutional layers are seldom investigated due to their high dimensionality and lack of global representation. Therefore, we introduced a channel attention mechanism adaptive to recalibrate the channel-wise features by considering dependencies among feature channels to strengthen the image features that are import to the classification tasks. Extensive experiments show that our model with attention mechanism achieves better accuracy on the sunflower seed images dataset compared with several classical networks.
Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.
In order to effectively detect the defects for fabric image with complex texture, this paper proposed a novel detection algorithm based on an end-to-end convolutional neural network. First, the proposal regions are generated by RPN (regional proposal Network). Then, Fast Region-based Convolutional Network method (Fast R-CNN) is adopted to determine whether the proposal regions extracted by RPN is a defect or not. Finally, Soft-NMS (non-maximum suppression) and data augmentation strategies are utilized to improve the detection precision. Experimental results demonstrate that the proposed method can locate the fabric defect region with higher accuracy compared with the state-of- art, and has better adaptability to all kinds of the fabric image.