Bar steel surface defects detection is very important to steel production and quality control. Many traditional computer vision methods have been applied to industrial defects detection, but they are usually environmentally sensitive and not robust enough. In this paper, a deep learning defects detection method based on Faster Region Convolutional Neural Networks (Faster R-CNN) is proposed. Firstly, to solve the problem of missed detection of a large number of small defects, we introduce Weighted Region of Interest (RoI) Pooling instead of RoI pooling, which eliminates the area misalignment caused by the two quantization processes in the latter, and the small defects detection rate is significantly improved. Secondly, considering that most of the defects are irregular in shape, we use deformable convolution in upper layers to adapt to various shapes by learning the positional offset in convolution. Thirdly, owing to the diversity of bar steel defects, multi-scale feature extraction network with Feature Pyramid Networks (FPN) is proposed to build feature pyramids. Finally, we propose Strict-Non-Maximum Suppression (Strict-NMS) algorithm to reduce overlapping bounding boxes as much as possible. Experiments on defect datasets in real industrial environments show that the detection rate of this method can reach 97%, which is much higher than state-of-the-art methods.

High resolution remote sensing image change detection plays an important role in areas such as land and resources investigation, natural disaster prediction, and military strategy research. Current change detection methods often focus on extracting more discriminative features, while ignoring the information loss and imbalance problems in the process of feature fusion, which results in weakness on small change objects and edge pixels of change objects. In this letter, a simple but efficient network architecture, ECFNet, for change detection in remote sensing image is proposed. By constraining the number of feature channels in the fusion process by FCM, ECFNet can better utilize the fine-grained information in the multi-scale feature map for result prediction, which not only improves the detection performance of small objects, but also reduces the false detection around the edge pixels of change objects. Experiments on the DSIFN-CD test set show that ECFNet achieves state-of-the-art results with a small amount of computation.

Text plays an important role in daily life because of its rich information, thus automatic text detection in natural scenes has many attractive applications. However, detecting and recognising such text is always a challenging problem. In this study, the authors propose a method which extends the widely‐used stroke width transform by two steps of edge analysis, namely candidate edge recombination and edge classification. A new method that recognises text through candidate edge recombination and candidate edge recognition is also proposed. In the step of candidate edge recombination, they use the idea of over‐segmentation and region merging. To separate text edge from background, the edge of the input image is first divided into small segments. Then, neighbour edge segments are merged, if they have similar stroke width and colour. Through this step, each character is described by one candidate boundary. In the step of boundary classification, candidate boundaries are aggregated into text chains, followed by chain classification using character‐based and chain‐based features. To recognise text, the grey image is extracted based on the location of each candidate edge after the step of candidate edge recombination. Then, histogram of gradient features and a classifier are used to recognise each character. To evaluate the effectiveness of their method, the algorithm is run on the ICDAR competition dataset and Street View Text database. The experimental results show that the proposed method provides promising performance in comparison with the existing methods.

Defect detection on bar surface is a challenging task due to the complex and variable bar surface conditions. Traditional pattern recognition methods are widely used to detect defects in the industry, however most of existing methods are not very universal for all kinds of defects. Meanwhile because of the limited number of defective samples, traditional deep learning methods are not very effective in practice. This paper addresses these issues and proposes a novel few-shot learning method which combines with attention mechanism. Our method is built by a Convolutional Neural Network (CNN) which extracts image features, and a Relation Network (RN) which calculates the similarity score between a pair of images, predicts image categories through similarity scores. Firstly, in order to extract more effective and discriminative features, we introduced Squeeze-and-Excitation Networks (SENet) as an attention module into our method which can enhance effective features and weaken invalid features. Secondly, unlike traditional object detection techniques which mainly focus on foreground information, background information is also necessary in our method, because we need to utilize background information to distinguish pseudo and real defects. So in our method, we replaced Max-Pooling with Mean-Pooling. Finally, in order to solve the low efficiency of parameter update caused by sharp dropping of loss function values on our dataset, we use L1Loss and BCELoss to replace Mean square error loss function. Experiment results show that the proposed method can achieve an average accuracy rate of 97.25% on our data set, increased by 7.92% compared with state-of-the-art.

With the development of deep learning, very high-resolution remote sensing image change detection (VHRCD) methods are becoming more popular. However, the most existing change detection methods are not good at processing edge details and small target detection. To this end, in this paper, InterFormer, a bidirectional interactive framework based on transformer is proposed to find small changes and extract more accurate edge information of the change area. First, we designed an asymmetric Interaction Attention Module (IAM) to identify the edge details for the bi-temporal image. The IAM fully leverages the benefits of self-attention, performing feature fusion during feature extraction. This approach improves edge feature extraction capability and reduces the number of parameters, compared to other transformer methods. Second, we designed a global attention based feature fusion module called GFFM to enhance the detection performance of small targets. The GFFM further improves small target detection ability by augmenting the network’s selectivity to spatial information during feature fusion. The method is applicable to scenarios involving small changes and possesses enhanced edge detection capabilities. Our method outperforms state-of-the-art counterparts on three public benchmarks and has fewer parameters.

To ensure safe and efficient intelligent transportation systems (ITS), autonomous driving systems must have excellent abilities about object detection and environmental perception. Fusing data from multiple sensors can overcome inherent limitations of single-sensor perception in 3D object detection for autonomous driving. LiDAR is great at pinpointing objects but doesn’t capture their velocity. Radar, on the other hand, accurately measures velocity but doesn’t provide height details. Fusing Radar and LiDAR can extend the detection range and improve the detection performance for dynamic objects. Nevertheless, direct integration of two sensors for performance improvement is hindered by different data characteristics and noise distributions. To address this, we propose a novel fusion framework termed LiDAR and Pseudo 4D-Radar fusion under Bird’s-Eye-View, dubbed L4R-BEVFusion, to overcome the challenge of multi-modal fusion. Specifically, Radar is a sensor that lacks object height information, we firstly design a pseudo 4D-Radar generation process that includes the sparse to dense(S2P) module and the height completion(RHC) module to transform the original 3D-Radar feature map into pseudo 4D-Radar feature map that is more dense and has enriched height information. Secondly, the fusion framework encodes the LiDAR features and the pseudo 4D-Radar features into the same Bird’s-Eye-View(BEV) through cross-guided BEV encoder(CGBE) module. Extensive experiments show that L4R-BEVFusion achieves state-of-the-art performance (71.3% NDS and 66.7% mAP) for detecting dynamic objects(only use LiDAR and Radar) on the NuScenes dataset.