To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the proposed model introduces the designed Detail Feature Retainer (DFR) into the original RTDETR backbone to capture and retain local details. Subsequently, based on the Mamba architecture, the Visual State Space (VSS) module is integrated to enhance global attention while reducing the original quadratic complexity to a linear level. Furthermore, by exploiting the deformable attention mechanism, which dynamically adjusts reference points, the model achieves precise localization of target defects and improves the accuracy of the transformer in complex visual tasks. Meanwhile, a receptive field synthesis mechanism is incorporated to enrich multi-scale semantic information and reduce parameter complexity. In addition, the scheme proposes a novel Multi-frequency Aggregation and Diffusion feature composite paradigm (MFAD-feature composite paradigm), which consists of the Aggregation Diffusion Fusion (ADF) module and the Refiner Feature Composition (RFC) module. It aims to strengthen features with fine-grained awareness while preserving a certain level of global attention. Finally, the Wise IoU (WIoU) dynamic nonmonotonic focusing mechanism is used to reduce competition among high-quality anchor boxes and mitigate the effects of the harmful gradients from low-quality examples, thereby concentrating on anchor boxes of average quality to promote the overall performance of the detector. Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. The experimental results show that our approach achieves the 97.0% and 51.0% performance in mean Average Precision (mAP)@0.5 and mAP@0.5:0.95, respectively, which significantly outperforms the original RTDETR. Moreover, the model reduces the number of parameters by approximately 18.2% compared to the original RTDETR.

Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in image classification tasks, CNN-based hyperspectral face recognition methods are worthy of further exploration. However, hyperspectral imaging poses new challenges including high data dimensionality and interference between bands on spectral dimension. High data dimensionality can result in high computational costs. Moreover, not all bands are equally informative and discriminative. The usage of a useless spectral band may even introduce noises and weaken the performance. For the sake of solving those problems, we proposed a novel CNN framework, which adopted a channel-wise attention mechanism and Lasso algorithm to select the optimal spectral bands. The framework is termed as the sparse spectral channel-wise attention-based network (SSCANet) where the SSCA-block focuses on the inter-band channel relationship. Different from other methods which usually select the useful bands manually or in a greedy fashion, SSCA-block can adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing less useful ones. Especially, a Lasso constraint strategy can zero out the bands during the training of the network, which can boost the training process by making the weights of bands sparser. Finally, we evaluate the performance of the proposed method in comparison of other state-of-the-art hyperspectral face recognition algorithms on three public datasets HK-PolyU, CMU, and UWA. The experimental results demonstrate that SSCANet based method outperforms the state-of-the-art methods for face recognition on the benchmark.

A micro-expression (ME), as a spontaneous facial expression, usually occurs instantaneously and is difficult to disguise after an emotion-evoking event. Numerous convolutional neural network (CNN)-based models have been widely explored to recognize MEs for their strong local feature representation ability on images. However, the main drawback of the current methods is their inability to fully extracting holistic contextual information from ME images. To achieve efficient ME learning representation from diverse perspectives, this paper uses Transformer variants as the main backbone and the dual-branch architecture as the main framework to extract meaningful multi-modal contextual features for ME recognition (MER). The first branch leverages an optical flow operator to facilitate the motion information extraction between ME sequences, and the corresponding optical flow maps are fed into the Swin Transformer to acquire motion–spatial representation. The second branch directly sends the apex frame in one ME clip to Mobile ViT (Vision Transformer), which can capture the local–global features of MEs. More importantly, to achieve the optimal feature stream fusion, a CAB (cross attention block) is designed to interact the feature extracted by each branch for adaptive learning fusion. The extensive experimental comparisons on three publicly available ME benchmarks show that the proposed method outperforms the existing MER methods and achieves an accuracy of 81.6% on the combined database.