NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search

Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed, which consists of repeatedly stacked basic novel cells with searchable attention operations. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better. This method can design high-performance and lightweight defect detection networks with data scarcity in industrial scenarios. The experimental results on four datasets demonstrate that the proposed method achieves superior performance and a relatively lighter model size compared to other competitive methods, including both manual and NAS-based approaches

Constructing long‐cycling crystalline C 3 N 4 ‐based carbonaceous anodes for sodium‐ion battery via N configuration control

Carbon nitrides with two‐dimensional layered structures and high theoretical capacities are attractive as anode materials for sodium‐ion batteries while their low crystallinity and insufficient structural stability strongly restrict their practical applications. Coupling carbon nitrides with conductive carbon may relieve these issues. However, little is known about the influence of nitrogen (N) configurations on the interactions between carbon and C3N4, which is fundamentally critical for guiding the precise design of advanced C3N4‐related electrodes. Herein, highly crystalline C3N4 (poly (triazine imide), PTI) based all‐carbon composites were developed by molten salt strategy. More importantly, the vital role of pyrrolic‐N for enhancing charge transfer and boosting Na+ storage of C3N4‐based composites, which was confirmed by both theoretical and experimental evidence, was spot‐highlighted for the first time. By elaborately controlling the salt composition, the composite with high pyrrolic‐N and minimized graphitic‐N content was obtained. Profiting from the formation of highly crystalline PTI and electrochemically favorable pyrrolic‐N configurations, the composite delivered an unusual reverse growth and record‐level cycling stability even after 5000 cycles along with high reversible capacity and outstanding full‐cell capacity retention. This work broadens the energy storage applications of C3N4 and provides new prospects for the design of advanced all‐carbon electrodes

Crystallinity engineering of carbon nitride protective coating for ultra‐stable Zn metal anodes

Ineffective control of dendrite growth and side reactions on Zn anodes significantly retards commercialization of aqueous Zn-ion batteries. Unlike conventional interfacial modification strategies that are primarily focused on component optimization or microstructural tuning, herein, we propose a crystallinity engineering strategy by developing highly crystalline carbon nitride protective layers for Zn anodes through molten salt treatment. Interestingly, the highly ordered structure along with sufficient functional polar groups and pre-intercalated K+ endows the coating with high ionic conductivity, strong hydrophilicity, and accelerated ion diffusion kinetics. Theoretical calculations also confirm its enhanced Zn adsorption capability compared to commonly reported carbon nitride with amorphous or semi-crystalline structure and bare Zn. Benefiting from the aforementioned features, the as-synthesized protective layer enables a calendar lifespan of symmetric cells for 1100 h and outstanding stability of full cells with capacity retention of 91.5% after 1500 cycles. This work proposes a new conceptual strategy for Zn anode protection

Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by uneven illumination and improve the visual quality of such images. However, these methods suffer from the loss of defect-discriminative information and a high computational burden. To address the above issues, we propose a joint-prior-based uneven illumination enhancement (JPUIE) method. Specifically, a semi-coupled retinex model is first constructed to accurately and effectively eliminate uneven illumination. Furthermore, a multiscale Gaussian-difference-based background prior is proposed to reweight the data consistency term, thereby avoiding the loss of defect information in the enhanced image. Last, by using the powerful nonlinear fitting ability of deep neural networks, a deep denoised prior is proposed to replace existing physics priors, effectively reducing the time consumption. Various experiments are carried out on public and private datasets, which are used to compare the defect images and enhanced results in a symmetric way. The experimental results demonstrate that our method is more conducive to downstream visual inspection tasks than other methods