Detectability Research Articles

Face processing in animal models: implications for autism spectrum disorder.

Processing facial features is crucial to identify social partners (prey, predators, or conspecifics) and recognize and accurately interpret emotional expressions. Numerous studies in both human and non-human primates provided evidence promoting the notion of inherent mechanisms for detecting facial features. These mechanisms support a representation of faces independent of prior experiences and are vital for subsequent development in social and language domains. Moreover, deficits in processing faces are a reliable biomarker of autism spectrum disorder, appearing early and correlating with symptom severity. Face processing, however, is not only a prerogative of humans: other species also show remarkable face detection abilities. In this review, we present an overview of the current literature on face detection in vertebrate models that could be relevant to the study of autism.

The failure mode and effect analysis of Islamic banking product marketing in Indonesia

Purpose This study aims to use a risk management approach to analyze the factors influencing the marketing failure of Islamic banking products in Indonesia. Design/methodology/approach Data were collected from respondents to be involved in the risk assessment of failure based on the criteria of severity score, likelihood of occurrence and detection ability of employees. The dominant factors were grouped into the number of new customers and transaction value categories. The failure mode and effect analysis (FMEA) method was used to identify the highest risk dominant factors. Findings The study results show critical factors and prioritized for immediate improvement. The number of new customers can be identified based on the following factors: occupation of prospective customers; income level of prospective customers; the lifestyle of the prospective customer. Transaction value can be identified based on the following factors: total balance; the level of convenience in transactions; turnover of funds; profitability; and risk management. Practical implications The Islamic banking industry in Indonesia needs to consider the importance of suitability: target market with the products offered, considering the income level of customers with suitable products, the level of profit sharing expected by consumers, improving customer education about Islamic banking products, the capabilities of financing staff and staff capabilities on risk management in Islamic banking products and services. Originality/value To the best of the author’s knowledge, this research is the first to apply FMEA in analysing marketing failures of Islamic banking products and services.

Utilization of multiplex polymerase chain reaction for simultaneous and rapid detection of viral infections from different ocular structures

The impact of viral keratitis (VK) on individuals and society is notable. Early diagnosis and treatment are crucial in managing viral keratitis effectively. Timely intervention with antiviral medications and supportive care can help mitigate the severity of the infection and improve visual outcomes. We examined the prevalence of varicella-zoster virus (VZV), herpes simplex virus type 1 (HSV-1), adenovirus (AdV) and herpes simplex virus type 2 (HSV-2) in patients suspected for ocular infections. Patients included in the study exhibited various clinical manifestations indicative of ocular pathology, such as infectious keratitis, corneal scar, endogenous endophthalmitis, panuveitis, endothelitis, stromal edema, and other relevant conditions. Four different types of tear fluid, corneal samples epithelium, aqueous humor and vitreous humor were taken. After genome extraction, multiplex real-time PCR was used for diagnosis of viruses. 48 (29.6%) out of the total of 162 (100%) eye specimen were positive. The dominant prevalence was VZV (12.3%) and HSV-1 (11.7%) followed by AdV (4.9%) and HSV-2 (0.6%). There were 4 (8.3%) coinfections within the samples (HSV-1 and VZV). Aqueous humor samples demonstrated superior virus detection ability and our only HSV-2 positive sample was from aqueous humor. The utilization of multiplex real-time PCR assays in differential diagnosis of VK holds promise for expeditious diagnoses while also preventing unwarranted antibiotic prescriptions. Moreover, the aqueous humor appears to be a more sensitive site for detecting viral keratitis.

"Three-in-One": Ultrasensitive Lateral Flow Immunoassay Driven by Magnetic Enrichment and Photothermal Signal Amplification.

Conventional lateral flow immunoassay (LFIA) usually suffers from poor antimatrix interference, unsatisfactory sensitivity, and lack of quantitative ability for target analyte detection in food matrices. In response to these limits, here, multifunctional nanomaterial ZnFe2O4 nanoparticles (ZFOs) were developed and integrated into LFIA for powerful magnetic separation/enrichment and colorimetric/photothermal target sensing. Under optimum conditions, the detection for clenbuterol (CL) with magnetic enrichment achieves 9-fold higher sensitivity compared to that without enrichment and 162-fold higher sensitivity compared to that based on traditional colloidal golds. Attributing the improved performances of ZFOs, CL can be detected at ultralow levels in pork and milk with 10 min of immunoreaction time. The vLODs were 0.01 μg kg-1 for two modes, and the cutoff values of CL were about 5 and 3 μg kg-1, respectively. More importantly, the enrichment ZFO-mediated LFIA (ZE-LFIA) exhibits a similar limit of detection (LOD) in both buffer solution and food matrix, demonstrating a universal resistance to the food matrix. The multitudinous performance merits of this ZE-LFIA with high sensitivity, matrix tolerance, accuracy, and specificity have ensured a broad application potential for target detection of clenbuterol and can serve as an experience for other veterinary drug residues' detection.

Fully automated OCT-based tissue screening system.

This study introduces an innovative optical coherence tomography (OCT) imaging system dedicated to high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep-learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields.

Flexible dual-mode pressure sensor for simultaneous dynamic-static sensing with wide-range detection and ultra-fast response speed

Flexible pressure sensors play a vital role in advancing the Internet of Things (IoT). However, piezoelectric sensors are limited in static pressure detection and suffer in achieving high flexibility and high sensitivity simultaneously, while piezoresistive sensors typically have slow response speeds and insensitive high-frequency detection ability. Here, we select a novel basalt fiber fabric (BFF) that can withstand high temperatures exceeding 1000 °C as a new substrate to enable one-step fabrication of piezoelectric layer based on coherently grown Pb(Zr0.52Ti0.48)O3 (PZT) thin films via a simple dipping method. The high piezoelectric coefficient g33 of 362.5 mV·m·N−1 and the unique core-sheath structure induced stress concentration on the PZT sheath enable the flexible piezoelectric layer (bending radius < 1.45 mm) to exhibit high sensitivity (voltage sensitivity of 2.766 V/kPa within the range of 0.11–11.11 kPa) and fast response speed (11 ms). The piezoresistive layer consisting of a three-dimensional porous elastic carbon nanotube-polyurethane sponge provides a detection range comparable to piezoelectric layer (0.11–66.67 kPa). A dual-mode flexible pressure sensor (DMFS) has been developed by integrating the piezoelectric layer with the piezoresistive layer. The DMFS exhibits an extended detection range compared to previous dual-mode pressure sensors, rapid response speed, and high sensitivity, which enables simultaneous detection of both dynamic and static pressures. The DMFS can be used for keyboard and movement posture recognition, which has significant application prospects in human-machine interaction.

Eco-friendly and sustainable synthesis of carbon quantum dots from waste sulfuric acid of alkylation

Waste sulfuric acid of alkylation (WSAA) poses a challenge in industrial alkylation oil production due to its substantial yield and high treatment costs. This study introduces a novel hydrothermal method that ingeniously couples the treatment of WSAA with the synthesis of high-quality carbon quantum dots (CQDs), offering a cost-effective and controllable approach. Through this method, the particle size and surface functional groups of the resulting CQDs can be precisely regulated. The average particle size tunes from 17.97 nm to 2.42 nm via increasing the hydrothermal temperature, and nitrogen-containing groups can be introduced through adding nitrogen sources during hydrothermal process. The prepared CQDs exhibit notable performance in photocatalysis and heavy metal detection, such as CQDs modified graphite carbon nitride has improved photocatalytic degradation ability and Hg (II) detection ability. Additionally, the environmental impact of the proposed method is substantially minimized, demonstrating a smaller footprint on ecosystems compared to conventional industrial disposal processes. Moreover, the economic cost associated with the method is significantly reduced by approximately 48.4 %, further highlighting its efficiency. Utilizing WSAA as a raw material for CQDs not only facilitates the recycling and sustainable utilization of waste but also enables low-cost production of high-performance carbon materials, presenting a mutually beneficial approach.

Hazelnut proteins detection in cookies using an immersible label-free photonic chip sensor

Rapid and sensitive methods to detect allergenic proteins in foods at the point-of-need could help to protect allergic consumers from life-threatening accidental exposure. In this context, the development of a label-free optical immunosensor for detecting hazelnut proteins in cookies is presented. The sensor is based on silicon photonic chips containing two integrated Mach-Zehnder interferometers (MZI) with light input and output on one side of the chip, and sensing window openings on the other side. Coupling with a white-light LED and a spectrometer for signal recording is achieved via a bifurcated fiber. The sensing window of one of the MZIs is modified with a mouse monoclonal antibody against hazelnut proteins, while the other sensing window is modified with bovine serum albumin to serve as blank. The assay follows a two-step sandwich immunoassay format, involving a 10-min reaction with the hazelnut proteins calibrator or cookie sample, followed by a 2-min reaction with a mixture of monoclonal antibodies. All reactions were performed by immersing the chip side where the MZIs windows are located into the solutions. The recorded spectrum is processed in real-time to transform the spectrum shifts due to immunoreactions taking place on the sensing windows of the two MZIs into phase shifts. The developed assay detects hazelnut proteins at concentrations as low as 25 ng/mL in calibrators prepared in buffer or in aqueous extract of hazelnut-free cookies. Additionally, cookies containing hazelnuts were analyzed demonstrating the sensor ability for fast and sensitive detection of the hazelnut proteins in commercial products.

Highly sensitive and specific uranyl ion detection by a fluorescent sensor containing uranyl-specific recognition sites

Uranium pollution has become a serious threat to human health and environmental safety, making the detection of environmental uranium contamination of great importance. The sensitive and specific detection of uranyl ions, which are the dominant form of uranium in the environment, depends on the specific recognition of uranyl ions by chemical groups. In this study, a novel fluorescent sensor containing a highly specific uranyl ion recognition group is synthesized via the reaction of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) and 1,1,2,2-tetra(4-carboxylphenyl)ethylene (TPE-(COOH)4). Owing to the effects of aggregation-induced emission (AIE) and intramolecular charge transfer (ICT), the fluorescent sensor, named TPE-EDC, exhibits significant fluorescent properties in aqueous environments. The binding of uranyl ions by specific recognition groups in TPE-EDC leads to a decrease in the ICT effect, thus causing a significant reduction in the emission intensity of TPE-EDC. The attenuation of the fluorescence intensity of TPE-EDC shows an excellent linear relationship with an increase in uranyl ion concentration. TPE-EDC exhibits ultra-sensitive and ultra-selective detection ability for uranyl ions with an ultra-low detection limit of 69 pmol/L and an ultrashort response time of 30 s. These high detection performances render the fluorescent sensor TPE-EDC a promising candidate for early warning of uranium pollution.

A Time-Series Feature-Extraction Methodology Based on Multiscale Overlapping Windows, Adaptive KDE, and Continuous Entropic and Information Functionals

We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Density Function (PDF) associated with a time signal in the context of non-parametric Kernel Density Estimation (KDE). We illustrate the applicability of this method for anomaly detection in time signals. Specifically, our approach combines a non-parametric kernel density estimator with overlapping windows of various scales. Regarding the parameters involved in the KDE, it is well-known that bandwidth tuning is crucial for the kernel density estimator. To optimize it for time-series data, we introduce an adaptive solution based on Jensen–Shannon divergence, which adjusts the bandwidth for each window length to balance overfitting and underfitting. This solution selects unique bandwidth parameters for each window scale. Furthermore, it is implemented offline, eliminating the need for online optimization for each time-series window. To validate our methodology, we designed a synthetic experiment using a non-stationary signal generated by the composition of two stationary signals and a modulation function that controls the transitions between a normal and an abnormal state, allowing for the arbitrary design of various anomaly transitions. Additionally, we tested the methodology on real scalp-EEG data to detect epileptic crises. The results show our approach effectively detects and characterizes anomaly transitions. The use of overlapping windows at various scales significantly enhances detection ability, allowing for the simultaneous analysis of phenomena at different scales.

Towards large-scale single-shot millimeter-wave imaging for low-cost security inspection

Millimeter-Wave (MMW) imaging is a promising technique for contactless security inspection. However, the high cost of requisite large-scale antenna arrays hinders its widespread application in high-throughput scenarios. Here, we report a large-scale single-shot MMW imaging framework, achieving low-cost high-fidelity security inspection. We first analyzed the statistical ranking of each array element through 1934 full-sampled MMW echoes. The highest-ranked elements are preferentially selected based on the ranking, building the experimentally optimal sparse sampling strategy that reduces antenna array cost by one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, realizing robust and accurate MMW image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at such a low sampling ratio. With the strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging.

Self and other-perceived deception detection abilities are highly correlated but unassociated with objective detection ability: Examining the detection consensus effect

Subjective lying rates are often strongly and positively correlated. Called the deception consensus effect, people who lie often tend to believe others lie often, too. The present paper evaluated how this cognitive bias also extends to deception detection. Two studies (Study 1: N = 180 students; Study 2: N = 250 people from the general public) had participants make 10 veracity judgments based on videotaped interviews, and also indicate subjective detection abilities (self and other). Subjective, perceived detection abilities were significantly linked, supporting a detection consensus effect, yet they were unassociated with objective detection accuracy. More overconfident detectors—those whose subjective detection accuracy was greater than their objective detection accuracy—reported telling more white and big lies, cheated more on a behavioral task, and were more ideologically conservative than less overconfident detectors. This evidence supports and extends contextual models of deception (e.g., the COLD model), highlighting possible (a)symmetries in subjective and objective veracity assessments.

Preparation and chromatographic evaluation of hydrophilic polymer brushes grafted-silica with post modification of silicon/carbon dots as a green liquid chromatography stationary phase.

Polar stationary phases were prepared by grafting hydrophilic acrylamide (Am) polymer brushes with post modification of carbon dots (CDs) and silicon dots (SiDs) onto SiO2 particles. The prepared stationary phases, SiO2-PAm-CDs, SiO2-PAm-CDs/SiDs, and SiO2-PAm-SiDs, were packed as chromatographic columns, respectively. Using nucleic bases, organic acids, and β-agonists as targetsubstances to investigate the influence of chromatographic conditions on retentionand separation, the packed columns showed the partitioning and adsorption of mixed retention behavior in hydrophilic interaction liquid chromatography mode and successfully separated thepolar compounds. Most importantly, under per aqueous liquid chromatography mode (using 100% water as mobile phase), those columns still had goodseparation ability toward nucleic bases, β-agonist, and organic acids. Because AM isa temperature-sensitive monomer, the resulting van't Hoff curves exhibited a nonlinear relationship, having temperature-responsive chromatographic characteristic under pure water separation. Hence, building on temperature-sensitive characteristics and pure water of separation conditions, the separation selectivity toward hydrophilic compounds greatly improved. Compared with the commercial hydrophilic columns, the efficiency of our developed column had the superior ability in separation and detection of betaine in Goji berry with the enhanced resolution achieved in the proposedgreen separation method(just using pure water as mobile phase).

The Big Role of Data Analytics in the Industrial Internet of Things

An IoT architecture is presented in the article which is a predictive way to maintain a group of buses using J1939 sensor data[1]. The authors adopted a minimally viable prototype (MVP) at the Socie´te´ de Transport de l’Outaouais (STO) garage in Gatineau, Canada, which was able to capture around 1 GB of uncompressed J1939 data per bus every day.[1] Data is a combination of the sensor reading and consists of data for the wheel speed, vehicle distance, driver pedal positions, engine information, oil information, coolant information, and transmission fluid information. The authors are trying to come up with a variant of Consensus Self-Organized Models (COSMO) named ICOSMO, which can self-fit the sensor selection for anomaly detection. The fourth industrial revolution has changed the face of ancient manufacturing by bringing in new technologies such as sensors, AI, IoT, and big data analytics. This equipment is used to convert factories into intelligent and interconnected systems that generate a huge amount of data. The data collected, when properly processed, can be utilized to improve decision making and reliability and safety. In addition, it makes predictive maintenance inevitable, thus minimizing the downtime and optimization of the performance[2]. Industrial big data is identified by the ”5V” elements: volume, velocity, variety, veracity, and value. Due to these characteristics, traditional data processing techniques become inadequate, so these circumstances require novel approaches to the treatment of the numerous and diverse data types that are produced in industry 4.0. The Proposed ICOSMO Infrastructure for IoT Predictive Maintenance consists of the following key activities: 1) finding out which sensor classes are the reasons for the faulty components, 2) adaptation of the sensor class (SC) and the sensor prediction capability (SPC) using fault detection abilities and 3) COSMO sensor configuration has to be rearranged after a certain period of time. ICOSMO has been verified with data from a minimally viable prototype (MVP) deployed on buses at the Socie´te´ de Transport de l’Outaouais in Canada. Furthermore, the interviewees said that the framework also includes techniques for the composition and the characterization of industrial big data, which is essential to make a practical predictive maintenance system that can work in smart manufacturing environments

Modified Gold Screen-Printed Electrodes for the Determination of Heavy Metals.

Screen-printed electrodes (SPEs) are reliable, portable, affordable, and versatile electrochemical platforms for the real-time analytical monitoring of emerging analytes in the environmental, clinical, and agricultural fields. The aim of this study was to evaluate the electrochemical behavior of gold screen-printed electrodes (SPGEs) modified with molecules containing amino (Tr-N) or α-aminophosphonate (Tr-P) groups for the selective and sensitive detection of the toxic metal ions Pb2+ and Hg2+ in aqueous samples. After optimizing the analytical parameters (conditioning potential and time, deposition potential and time, pH and concentration of the supporting electrolyte), anodic square wave stripping voltammetry (SWASV) was used to evaluate and compare the electrochemical performance of bare or modified electrodes for the detection of Hg2+ and Pb2+, either alone or in their mixtures in the concentration range between 1 nM and 10 nM. A significative improvement in the detection ability of Pb2+ ions was recorded for the amino-functionalized gold sensor SPGE-N, while the presence of a phosphonate moiety in SPGE-P led to greater sensitivity towards Hg2+ ions. The developed sensors allow the detection of Pb2+ and Hg2+ with a limit of detection (LOD) of 0.41 nM and 35 pM, respectively, below the legal limits for these heavy metal ions in drinking water or food, while the sensitivity was 5.84 µA nM-1cm-2 and 10 µA nM-1cm-2, respectively, for Pb2+ and Hg2+. The reported results are promising for the development of advanced devices for the in situ and cost-effective monitoring of heavy metals, even in trace amounts, in water resources.

Near space detection technology combining van der Waals heterojunction photodetectors with long-range infrared target detection algorithms

To test the target detection accuracy of infrared detectors in practical applications, this study proposes a near space detection (NSD) technology that integrates van der Waals heterojunction photodetectors with long-range infrared target detection algorithms (ITDAs). A van der Waals heterojunction infrared photodetector (MoS2/CdTe) is prepared by combining molybdenum disulfide thin film and cadmium telluride substrate, and depositing gold electrodes on both substrates. A fusion algorithm of spatiotemporal target detection network and local contrast method is proposed for long-distance detection of weak infrared targets, and a near-space infrared detection system is designed. The data showed that the MoS2/CdTe heterojunction photodetector had a detection range of 200–1700 nm and a response of 36.7 mA/W, demonstrating excellent optoelectronic performance. The false alarm rates of the long-distance ITDA were 0.006% and 0.009%, respectively, which are significantly better than the target detection algorithm based on local contrast. In the testing of the near-space infrared detection system, the errors in the shortwave and longwave field of view tests were 2.5% and 5.2%, respectively, and the resolution test errors were 3.6% and 3.7%. The infrared detection system meets the performance standards, has high detection ability and stability, and provides a more effective solution for NSD.

Application research on vector coherent frequency‐domain batch adaptive line enhancement in deep water

AbstractThe low frequency line spectrum noise radiated by ships has strong stability and is difficult to eliminate, which is the key information required for passive signal detection. A vector coherent frequency‐domain batch adaptive line enhancement method is proposed to address the issue of insufficient detection capability of traditional scalar adaptive line enhancement (ALE) algorithms for ship characteristic line spectra in complex deep‐sea environments. This method not only introduces the idea of frequency‐domain batch processing, but also uses synchronously collected sound pressure and particle velocity as dual input, fully utilising the coherence characteristics between vector channels to output high gain line spectrum signals and improve computational efficiency. In simulation and sea trial data validation, compared with the time‐domain vector coherent adaptive line enhancement algorithm, this method has shorter time consumption, higher efficiency, and can improve the detection ability of line spectrum signals under low signal‐to‐noise ratio conditions. The bearing estimation results output by this algorithm is also more accurate.

Exploiting floral signals: Olfactory crypsis and visual attraction in crab spider predatory strategies

AbstractPredation exerts a profound influence on the evolution of camouflage and detection abilities in both predators and prey. For instance, flower‐visiting spiders need to ensure their concealment when ambushing on flowers, which compels pollinators to check any spider cues cautiously before landing. Although numerous studies have examined the visual camouflage of spiders deceiving bees, little is known regarding whether spiders also employ olfactory camouflage. Additionally, the detection of spiders by dipteran insects is often overlooked, despite them being major non‐bee pollinators. Here, we explored the detection ability of dipteran pollinators and the crypsis skill of spiders from both olfactory and vision perspectives using the housefly Musca domestica L. (Diptera: Muscidae) as prey, the crab spider Ebrechtella tricuspidata (Fabricius) (Araneae: Thomisidae) as predators and chamomile Matricaria recutita L. (Asteraceae) as substrates. Our olfactory experiments revealed that experienced houseflies could respond to and avoid spider odour. However, this response did not appear to be innate, as naive individuals showed no aversion. Moreover, experienced houseflies did not have any avoidance behaviours to the mixed odours of spider and flower, indicating that spiders achieved olfactory crypsis utilizing floral scent. Our vision experiments and visual modelling demonstrated that houseflies could detect spiders positioned on flower. Surprisingly, instead of avoiding them, houseflies exhibited a preference for flowers occupied by female spiders, suggesting the attractive nature of female spider colouration. This paper provides evidence for the first time that crab spiders use floral scent to achieve olfactory crypsis and proposes a potential yellow‐signalling mechanism for crab spiders to attract insects.

Electrochemical sensor based on mesoporous g-C3N4/N-CNO/gold nanoparticles for measuring oxycodone

Oxycodone, often used as an analgesic, is a potent opioid. While its effectiveness has been proven in the control of moderate to acute pain, excessive use of oxycodone imposes heart failure, heart palpitations, reduction of red blood cells, bone pain, and even death. Therefore, monitoring the oxycodone concentration in blood is vital for emergency care. For this purpose, a novel electrochemical sensor was designed based on a glassy carbon electrode modified with mesoporous g-C3N4 (M-C3N4), carbon nano-onions doped with nitrogen (N-CNO), and gold nanoparticles. At first, the SEM and XRD techniques were employed to characterize prepared M-C3N4 and N-CNO samples. The electro-oxidation behavior of the oxycodone was evaluated by cyclic and differential pulse voltammetric methods. Based on the influence of the potential scanning rate and solution pH on the voltammetric response of oxycodone oxidation, a redox mechanism was proposed. A 16 nM detection limit was acquired for the oxycodone analysis with a linear response in the 0.05–150 µM range. This sensor showed a remarkable ability for oxycodone detection in plasma samples. The long-term stability, superior selectivity, and reproducibility of this sensor prove its ability to measure oxycodone accurately and precisely in authentic spices.

REDef-DETR: real-time and efficient DETR for industrial surface defect detection

Abstract Industrial surface defect detection is an important part of industrial production, which aims to identify and detecting various defects on the surface of product to ensure quality and meet customer requirements. With the development of deep learning and image processing technologies, the surface defect detection methods based on computer vision has become the mainstream method. However, the prevalent convolutional neural network-based defect detection methods also have many problems. For example, these methods rely on post-processing of Non-Maximum Suppression and have poor detection ability for small targets, which affects the speed and accuracy of surface defect detection in industrial scenarios. Therefore, we propose a novel DEtection TRansformer-based surface defect detection method. Firstly, we propose a Multi-scale Contextual Information Dilated module and fuse it into the backbone. The module is mainly composed of large kernel convolutions, which aims to expand the receptive field of the model, thus reducing the leakage rate of the model. Moreover, we design an efficient encoder which mainly contains two important modules, namely feature enhancement based on cascaded group attention module and efficient feature fusion module based on content-aware. The former module effectively enhances the high-level semantic information extracted by the backbone, thus enabling the model to better interpret features, and it can improve the problem of high computational cost of transformer encoder, thus increasing the detection speed. The latter module performs multi-scale feature fusion across the feature information of various scales, thus improving the detection accuracy of the model for small-size defects. Experimental results show that the proposed method achieves 80.6%mAP and 80.3FPS on NEU-DET, and 98.0%mAP and 79.4FPS on PCB-DET. Our proposed method exhibits excellent detection performance and achieves real-time and efficient surface defect detection capability to meet the needs of industrial surface defect detection.