Vision-based surface defect detection (SDD) for no-service aero-engine blades provides a fast and effective way to monitor product quality. Most existing detection algorithms for aero-engine blades are 1) based on CNN, including artificially designed non-maximum suppression (NMS) operations, and 2) focus on improving the detection accuracy rather than improving the inference speed and even ignoring the latter. To solve the above problems, we introduce a novel object detection paradigm, DEtection TRansformer (DETR), to design a novel network (SDD-DETR) with high accuracy for the SDD of aero-engine blades. To our knowledge, the paper is the first to introduce the DETR detector to SDD of aero-engine blades. While providing high accuracy, the inference speed of DETR remained slow due to self-attention operation and feed-forward network (FFN). Therefore, two lightweight modules have been designed for SDD of aero-engine blades: a progressive feature input multi-scale deformable attention module (PFI-MSDA) and a lightweight FFN (LW-FFN). PFI-MSDA hierarchically reduces the number of tokens input to the self-attention module, thereby reducing the time complexity of the self-attention layer. LW-FFN shrinks the complexity of multilayer perceptron. In addition, no parameter sharing of the detection head is utilized to compensate for the accuracy drop caused by the lightweight. Experiments verify that our method has the same AP and F1-score as DINO (a DETR-based detector), but our approach is lighter. Compared with DINO, the FLOPs are reduced by$113.4{G}$, the inference speed is increased by 42.4%, and the runtime memory usage is reduced by$5.9{G}$, which allows our method to be trained on low-end GPUs with more batch size, further improving the training efficiency. The code is available athttps://github.com/VDT-2048/SDD-DETR.Note to Practitioners—The motivation for this paper is to design a high-precision and high-inference speed visual detection method for the SDD of aero-engine blades. Most high-precision vision methods are based on the transformer framework. However, its high complexity and poor compatibility in deployment environments lead to slower detection speeds. Although the application object in this paper is aero-engine blades, it is also applicable in other fields of industry, such as rail detection, plate and strip steel detection, etc. However, the method proposed cannot be supported by deployment environments such as RKNN because of the deformable attention operator, so it takes a certain amount of time to be deployed and put into practical use. Currently, the frameworks of visual and language large models are based on transformers, consistent with the framework of our method, which makes extending our approach to large visual and multi-modal models more accessible.

Nondestructive testing of welding images is still a significant challenge due to the imaging characteristics of radiographic images and the extremely random distribution of welding defect types. The practical application of supervised methods for welding nondestructive testing encounters significant challenges due to the limited availability of densely annotated samples and the absence of prior information regarding unknown defects. Hence, this article first proposes a rapid screening method using only defect-free image training to screen suspected defect images and normal images of welds in real time. Specifically, we first combine the correspondence mechanism and the representation mechanism, aiming to: 1) alleviate the smoothing reconstruction behavior caused by small defects and weak texture defects in weld; and 2) mitigate the offset learning behavior resulting from the differences between natural images and industrial weld images in the normalizing flow method. We propose a memory-aware transformer-based encoder, thus improving representations of complex defect-free images. Moreover, a dual-decoder strategy is introduced, which remaps the latent dependencies generated by the encoder through a semantic correspondence mechanism and reconstruction-guided normalizing flow, enabling effective learning of knowledge from weld images. We apply this framework to an industrial case of weld images, the experimental results demonstrate that our method outperforms other existing approaches.

Deep neural networks have greatly improved the performance of rail surface defect segmentation when the test samples have the same distribution as the training samples. However, in practical inspection scenarios, the rail surface exhibits variations in appearance due to different service times and natural conditions. Conventional deep learning models show limited generalization in scenes with distribution differences. To address this problem, we propose a novel one-shot unsupervised domain adaptation framework. Specifically, we introduce a shape-consistent style transfer module that performs pixel-level distribution alignment between the training and test images. Based on the one-shot test image, the training image is reconstructed to have the same appearance as the test image. Meanwhile, we employ a multitask learning strategy to prevent content distortion of the reconstructed images. To improve the robustness of the model to distribution differences, we design an edge-aware defect segmentation model and train the model using the reconstructed training images. The experimental results show that our method effectively improves the robustness of the model to distribution differences and achieves satisfying results in the task of rail surface defect segmentation.

Automatic pavement defect segmentation is a big challenge because of the class diversity and extremely random distribution of defects. Most existing approaches focus on supervised strategies to achieve decent performance. Due to the difficulty of getting massive densely annotated samples and the limited prior knowledge of potential defects, these methods have significant bottlenecks in the actual pavement settings. This paper proposes a relevance-aware and cross-reasoning network (RCN) for anomaly segmentation of pavement defects, which can segment defects using merely non-defective images for training. A relevance-aware transformer-based encoder is first devised to model intrinsic interdependencies across local features, thus improving representations of complex non-defective images. Next, a dual decoder strategy is proposed to remap the encoder-generated latent dependencies at the local semantic and global detailed levels, respectively. Specifically, a cross-reasoning refinement module is built in the local decoder to reason the cross-relationship between spatial and channel dimensions. Finally, a context-aware abnormal distillation measurement is developed to evaluate the semantic reconstruction deviations during the inference. Under the guidance of semantic affinity, this measurement allows our model to highlight defective areas adaptively. Extensive experimental results on four datasets indicate that RCN outperforms other leading anomaly segmentation methods.

Pavement distress detection is of significance for road maintenance and traffic safety. Manual pavement distress detection suffers from high workloads, inefficiency, low accuracy, and high cost. To replace the manual operations in the pre-filling detection with the aim to improve efficiency and reduce cost, this paper proposes a three-stage automatic inspection and evaluation system for pavement distress based on improved deep convolutional neural networks (CNNs). First, the system integrates multi-level context information from the CNN classification model to construct discriminative super-features to determine whether there is distress in the pavement image and the type of the distress, so as to achieve rapid detection of pavement distress. Then, the pavement images with distress are fed into the CNN segmentation model to highlight the distress region with pixel-wise. In the segmentation model, a novel pyramid feature extraction module and a novel guidance attention mechanism are introduced. Finally, we evaluate the degree of pavement damage according to the segmentation results of the CNN segmentation model. In the experiments, we compare our classification model and segmentation model with other state-of-the-art methods on two pavement distress datasets, and the results demonstrate that the proposed models achieve out-performance on different evaluation metrics.