Detection In Complex Environments Research Articles

UV-Vis-NIR Broadband Self-Powered CuInS2/SnO2 Photodetectors and the Application in Encrypted Optical Communication.

Photodetectors (PDs) with broadband photoresponse can meet the demand for multiband detection in complex environments, overcoming the technological complexity issue of integrated narrow-band PDs. Self-powered heterojunction PDs having ultraviolet-visible-near-infrared broadband photoresponse were constructed by using SnO2 nanopillars and CuInS2 nanoflakes. The dimension, crystalline quality, and energy level structure of the SnO2 nanopillars were regulated by changing the concentration of Sn ions in the precursor solution. The optimized interfacial energy band structure of the heterojunction can increase the transfer ability of the photogenerated carrier. The optimum performance is achieved for the CuInS2/SnO2(0.025M) PD prepared at 0.025 M Sn ion concentration in the precursor solution with the responsivities of 1.15, 6.13, and 1.02 mA/W, and detectivities of 1.19 × 1010, 6.35 × 1010, and 1.02 × 1010 Jones under 254 nm solar-blind ultraviolet light, 475 nm visible light, and 940 nm near-infrared light. Furthermore, a proof-of-concept solar-blind ultraviolet-visible-near-infrared encrypted communication system utilizing a broadband self-powered CuInS2/SnO2 PD as the receiving terminal and solar-blind ultraviolet light, ultraviolet light, visible light, and near-infrared light as the carrier and encryption protocol is proposed. The PD has great potential for applications in the field of encrypted optical communication.

4AC-YOLOv5: an improved algorithm for small target face detection

In real scenes, small target faces often encounter various conditions, such as intricate background, occlusion and scale change, which leads to the problem of omission or misdetection of face detection results. To solve this puzzle, an improved algorithm of small target face detection 4AC-YOLOv5 is proposed. First, the algorithm by introducing a new layer to detect faces at a much smaller size, through the fusion of more shallow information, enhance the network perception of small objects, the accuracy of small target detection is improved; second, to improve the neck structure, to add the adaptive feature fusion network AFPN to replace FPN + PAN, to prevent the large information gap between non-adjacent Level to some extent, and to fully retain and integrate different scale characteristic information; and finally, improve the C3 module and propose a new multiscale residual module C3_MultiRes. Improving the expressive power of the network by introducing a multibranched structure and gradually increasing resolution somewhat reduces the complexity of the model calculation. The experimental results show that the precision of the improved model reached 94.54%, 93.08% and 84.98% in easy, medium and hard levels of WiderFace data set, respectively, and the results of detection are better than the original network. 4AC-YOLOv5 can meet the requirements of small target face detection in complex environment.

Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7

Wearing masks is an effective protective measure for residents to prevent respiratory infectious diseases when going out. Due to issues such as a small target size, target occlusion leading to information loss, false positives, and missed detections, the effectiveness of face mask-wearing detection needs improvement. To address these issues, an improved YOLOv7 object detection model is proposed. Firstly, the C2f_SCConv module is introduced in the backbone network to replace some ELAN modules for feature extraction, enhancing the detection performance of small targets. Next, the SPPFCSPCA module is proposed to optimize the spatial pyramid pooling structure, accelerating the model convergence speed and improving detection accuracy. Finally, the HAM_Detect decoupled detection head structure is introduced to mitigate missed and false detections caused by target occlusion, further accelerating model convergence and improving detection performance in complex environments. The experimental results show that improved YOLOv7 achieved an mAP of 90.1% on the test set, a 1.4% improvement over the original YOLOv7 model. The detection accuracy of each category improved, effectively providing technical support for mask-wearing detection in complex environments.

Floating object detection using double-labelled domain generalization

Detection of floating objects in complicated aquatic environments has a wide range of applications, but it confronts significant hurdles due to the imbalance in detection accuracy and efficiency, and low domain generalization performance. To address these issues, this study proposes a novel floating object detection method based on double-labelled domain generalization. First, the Single Shot Multibox Detector (SSD) is improved by replacing the backbone network with a lightweight feature extraction network, and dynamic feature pyramid network is introduced to balance accuracy and efficiency. Then, this study initializes the improved SSD network based on the double-labelled data of the source domain, and filters the feature extractor bias, classification bias and location bias using pseudo-labelling and feature projection based on the un-labelled source domain data to minimize the bias to improve the domain generalization performance. The proposed method is trained on a self-constructed floating object dataset and is compared with state-of-the-art methods based on multiple scenarios. The results show that the proposed method achieves better performance in double-labelled domain generalization and conventional domain generalization tasks compared to other methods, achieving 70.33%, 22.38 f/s and 85.29% and 17.81 f/s in accuracy and speed respectively, satisfying the need for multi-scale floating object detection in complex environments and also alleviating the data labelling problem. This work effectively solves the problem of slow detection of floating objects due to the complex model structure and low generalization ability, and provides support for the rapid detection of floating objects in complex scenarios and the promotion of technology applications.

Research on Surface Defect Detection of Strip Steel Based on Improved YOLOv7.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

An Aerial Image Detection Algorithm Based on Improved YOLOv5.

To enhance aerial image detection in complex environments characterized by multiple small targets and mutual occlusion, we propose an aerial target detection algorithm based on an improved version of YOLOv5 in this paper. Firstly, we employ an improved Mosaic algorithm to address redundant boundaries arising from varying image scales and to augment the training sample size, thereby enhancing detection accuracy. Secondly, we integrate the constructed hybrid attention module into the backbone network to enhance the model's capability in extracting pertinent feature information. Subsequently, we incorporate feature fusion layer 7 and P2 fusion into the neck network, leading to a notable enhancement in the model's capability to detect small targets. Finally, we replace the original PAN + FPN network structure with the optimized BiFPN (Bidirectional Feature Pyramid Network) to enable the model to preserve deeper semantic information, thereby enhancing detection capabilities for dense objects. Experimental results indicate a substantial improvement in both the detection accuracy and speed of the enhanced algorithm compared to its original version. It is noteworthy that the enhanced algorithm exhibits a markedly improved detection performance for aerial images, particularly under real-time conditions.

AOPPI: An adaptive obstruction perception pedestrian identifier for complex environments

Pedestrian detection plays a crucial role in ensuring traffic safety within the domain of computer vision. However, accurately detecting pedestrians in complex environments proves to be a challenge due to issues such as occlusion. To address this issue, this paper presents an end-to-end pedestrian detection model founded on the DEtection TRansformer (DETR) architecture, effectively managing occlusion scenarios. The proposed model comprises a backbone Convolutional Neural Network (CNN) and a Transformer network. The backbone CNN incorporates variable convolution and U-Net design principles to enhance feature extraction capabilities, particularly for occluded pedestrians. Additionally, our innovative Adaptive Occlusion-Aware Attention Mechanism (AOAM) is embedded within the Transformer network, allowing the model to dynamically adjust attention weights and enhance the localization and identification of occluded pedestrians. Extensive experiments on the Caltech and ETH datasets demonstrate the superior performance of our model compared to state-of-the-art approaches across four key evaluation metrics. This study provides effective methodologies and theoretical foundations for pedestrian detection in complex environments.

Ultrasensitive ion liquid-CNT vdW heterostructure interface via anion-cation pair switch for SO2 detection

Low dimensional van der Waals (vdW) interface shows great advantages for application in vapor detection, such as ultra-high sensitivity and excellent reversibility. However, detection of non-character REDOX capacity and low atmosphere concentration gas such as SO2 is more challengeable. Inspired by the high efficient gas adsorption performance and versatile ion pair combination of ionic liquids, we constructed a vdW heterostructure interface based polymeric ion liquid/SWCNT sensor with different ionic pairs for SO2 detection. Via the composites tuning of SWCNTs and poly(vinylbenzyl 3-ethylimidazolium)-based ionic liquids (PILs) modified with different anions of chloride (Cl-), boron tetrafluoride (BF4-), hexafluorophosphate (PF6-), and bis(trifluoromethanesulfonimide) ions (Tf2N-), stable vdW heterostructure interface was successfully constructed, as proved by SEM, FTIR, XPS and Raman spectra. Upon SO2 adsorption, the distance between the cation/anion was tuned at the heterostructure interface, which triggered great composite tightness changes and thus electrical signal changes of heterostructure interface due to the reversed electron transfer, proved by XPS, Raman spectra and dynamics simulations. Among the sensors composed of the different anions, PIL-BF4-SWCNT exhibited lowest detection limit 20.4 ppt for SO2, which is the best result ever reported. The sensor also show great selectivity, reversibility and stability, with 3.77% response for 512 ppt SO2 within 14 days. The new strategy will open a new way for selective, ultrasensitive and low power vapor detection in complex environment.

Ionic Liquid-Assisted Thermal Evaporation of Bimetallic Ag-Au Nanoparticle Films as a Highly Reproducible SERS Substrate for Sensitive Nanoplastic Detection in Complex Environments.

Nanoplastic particles are emerging as an important class of environmental pollutants in the atmosphere that have adverse effects on our ecosystems and human health. While many methods have been developed to quantitatively detect nanoplastics; however, sensitive detection at low concentrations in a complex environment remains elusive. Herein, we demonstrate a greener method to fabricate a surface-enhanced Raman spectroscopy (SERS) substrate consisting of self-assembled plasmonic Ag-Au bimetallic nanoparticle (NP) films for quantitative SERS detection of nanoplastics in complex media. The self-assembly of Ag-Au bimetallic NPs was achieved through thermal evaporation onto a vapor-phase compatible ionic liquid based on deep eutectic solvent over the growth substrate. The finite-difference time-domain simulation revealed that the localized field enhancement is strong in the gaps, which generate uniform SERS "hotspots" in the obtained substrate. Benefiting from highly accessible SERS "hotspots" at the gaps, the SERS substrate exhibits excellent sensitivity for detecting crystal violet with a limit of detection (LOD) as low as 10-14 M and excellent reproducibility (RSD of 5.8%). The SERS substrate is capable of detecting PET nanoplastics with LOD as low as 1 μg/mL and about 100 μg/mL in real samples such as tap water, lake water, diluted milk, and wine. Moreover, we also validated the feasibility of the designed SERS substrate for the practical detection of PET nanoplastics collected from commercial drinking water bottles, and it showed great potential applications for sensitive detection in actual environments.

A highly sensitive electrochemical sensing probe incorporating classical Berthelot's reaction and glassy carbon electrode for measuring ultra-trace levels of ammonia/ NH4+ in water

In quantifying NH3 and/or NH4+ ions, the wavelength-based spectrochemical methods involving formation of colored products of the Berthelot’s or Nessler reactions might be challenging due to auto-self absorbance, spectral overlap, and background scattering noise. Thus, the current study reports a renewable glassy carbon electrode (GCE) sensing probe combined with Berthelot’s reaction (indophenol formation) and adsorptive square - wave-anodic stripping voltammetry (Ads SWSV) at pH = 10 for detection of NH3 and/or NH4+ in water. The redox characteristics and the high surface coverage of the oxidation product of indophenol on the sensing platform suggested its use for NH3 and/or NH4+ detection. The electrochemical sensing probe for NH4+ displayed good linear relationship between 5.56 nM and 55.6 μM of NH4+ with limits of detection (LOD) and quantitation (LOQ) of 4.83 × 10-9 and 1.47 × 10-8 M, and sensing probe sensitivity of 1.27μA/μM−1 cm−2,respectively. The probe was applied for measuring NH3/NH4+in fresh and seawater samples, and the results were validated using standard ion chromatography (IC) and micro spectrophotometry assays. The assembled probe was also tolerably selective against interfering of other contaminants in a comparable potential window. Additionally, the probe has exceptional selectivity, long-term stability, and repeatability, and has good capacity to detect NH3 and/or NH4+ ions with high accuracy (recovery range = 97.14 ± 4.12–102.9 ± 4.7) in environmental water samples. The calculated Student texp and Fexp values (n = 5) were less than the tabulated ttab (2.78) and Ftab (6.39) at 95 % probability (P = 0.05, n = 5). The study offer high reliability towards NH3 and/or NH4+ detection in complex environments.

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.

Efficient Tobacco Pest Detection in Complex Environments Using an Enhanced YOLOv8 Model

Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model’s ability to extract features. The backbone network’s C2f model is replaced with the VoV-GSCSP module to reduce the model’s computational requirements. Experiments show the improved YOLOv8 model achieves high overall performance. Compared to the original model, model parameters and GFLOPs are reduced by 52.66% and 19.9%, respectively, while mAP@0.5 is improved by 1%, recall by 2.7%, and precision by 2.4%. Further comparison with popular detection models YOLOv5 medium (YOLOv5m), YOLOv6 medium (YOLOv6m), and YOLOv8 medium (YOLOv8m) shows the improved model has the highest detection accuracy and lightest parameters for detecting four common tobacco pests, with optimal overall performance. The improved YOLOv8 detection model proposed facilitates precise, instantaneous pest detection and recognition for tobacco and other crops, securing high-accuracy, comprehensive pest identification.

A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments

In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.

A lightweight CNN for multi-source infrared ship detection from unmanned marine vehicles

Infrared ship detection is of great significance due to its broad applicability in maritime surveillance, traffic safety and security. Multiple infrared sensors with different spectral sensitivity provide enhanced sensing capabilities, facilitating ship detection in complex environments. Nevertheless, current researches lack discussion and exploration of infrared imagers in different spectral ranges for marine objects detection. Furthermore, for unmanned marine vehicles (UMVs), e.g., unmanned surface vehicles (USVs) and unmanned ship (USs), detection and perception are usually performed in embedded devices with limited memory and computation resource, which makes traditional convolutional neural network (CNN)-based detection methods struggle to leverage their advantages. Aimed at the task of sea surface object detection on USVs, this paper provides lightweight CNNs with high inference speed that can be deployed on embedded devices. It also discusses the advantages and disadvantages of using different sensors in marine object detection, providing a reference for the perception and decision-making modules of USVs. The proposed method can detect ships in short-wave infrared (SWIR), long-wave infrared (LWIR) and fused images with high-performance and high-inference speed on an embedded device. Specifically, the backbone is built from bottleneck depth-separable convolution with residuals. Generating redundant feature maps by using cheap linear operation in neck and head networks. The learning and representation capacities of the network are promoted by introducing the channel and spatial attention, redesigning the sizes of anchor boxes. Comparative experiments are conducted on the infrared ship dataset that we have released which contains SWIR, LWIR and the fused images. The results indicate that the proposed method can achieve high accuracy but with fewer parameters, and the inference speed is nearly 60 frames per second (FPS) on an embedded device.

Opportunity and Challenge of Emerging COFs in Chemiresistive Sensing for Gas and Humidity

AbstractNovel 2D materials for chemiresistive sensors are attractive due to their large surface area and structure diversity. Covalent organic frameworks (COFs) have received much attention due to their unique properties, including the abundant choice of organic building units, permanent pore structure and crystallinity, which show huge potential of implement in gas and humidity detection in complex environment. Recently, the development of the synthesis and modification methods for COFs powder and film contributes to the sensitivity and compatibility in chemiresistive sensors and integrated devices. In the present review, the current synthesis methods of COFs powder and film as well as their application in the chemiresistive sensors are summarized. The current challenge and future modification strategy are discussed for the further development of COFs as active materials in gas and humidity sensors.

Education robot object detection with a brain-inspired approach integrating Faster R-CNN, YOLOv3, and semi-supervised learning.

The development of education robots has brought tremendous potential and opportunities to the field of education. These intelligent machines can interact with students in classrooms and learning environments, providing personalized educational support. To enable education robots to fulfill their roles, they require accurate object detection capabilities to perceive and understand the surrounding environment of students, identify targets, and interact with them. Object detection in complex environments remains challenging, as classrooms or learning scenarios involve various objects, backgrounds, and lighting conditions. Improving the accuracy and efficiency of object detection is crucial for the development of education robots. This paper introduces the progress of an education robot's object detection based on a brain-inspired heuristic method, which integrates Faster R-CNN, YOLOv3, and semi-supervised learning. By combining the strengths of these three techniques, we can improve the accuracy and efficiency of object detection in education robot systems. In this work, we integrate two popular object detection algorithms: Faster R-CNN and YOLOv3. We conduct a series of experiments on the task of education robot object detection. The experimental results demonstrate that our proposed optimization algorithm significantly outperforms individual algorithms in terms of accuracy and real-time performance. Moreover, through semi-supervised learning, we achieve better performance with fewer labeled samples. This will provide education robots with more accurate perception capabilities, enabling better interaction with students and delivering personalized educational experiences. It will drive the development of the field of education robots, offering innovative and personalized solutions for education.

Comparative study on object detection in visual scenes using deep learning

Object detection is a crucial aspect of computer vision, enabling machines to identify and locate various objects within images or videos. This paper provides an in-depth review of the subject, discussing its importance and applications across diverse fields, including autonomous vehicles, surveillance, augmented reality, healthcare, retail, and environmental monitoring. The object detection framework is outlined, highlighting key steps such as image acquisition, preprocessing, feature extraction, object detection models, and post-processing. Deep learning techniques have significantly improved object detection, making it more accurate and faster. Various state-of-the-art models, such as YOLOv4, YOLOv5, and MobileNetV3, are presented with their respective performance metrics. The paper also explores recent developments in object detection, including novel loss functions, neural architecture search (NAS), and advancements in handling challenging conditions like occlusions and low lighting. Despite the progress, there remain challenges in the field, such as improving object detection in complex environments. Looking to the future, the paper predicts that object detection models will become more accurate and versatile, capable of handling challenging conditions and detecting a wider range of objects. Deep learning will continue to play a vital role in advancing object detection, leading to further breakthroughs in the field. The provided references offer a comprehensive overview of the literature on object detection, making this paper a valuable resource for researchers and practitioners in the field.

Research on parking space detection algorithm in complex environments based on improved YOLOv7

In the context of complex parking environments, vehicle parking space detection faces challenges such as multi-scale, multi-angle, and occlusion issues, leading to low detection efficiency and problems with false positives and false negatives. In this study, we propose an improved vehicle parking space detection algorithm based on YOLOv7. Firstly, we enhance the convolutional layers by introducing the Mish activation function, thereby improving the model’s feature extraction capabilities and its ability to represent objects effectively. Secondly, we combine the parameter-free attention mechanism SimAM with feature pyramid modules and feature extraction modules to replace certain convolutional layers, thereby enhancing the reinforcement of critical parking space information and adaptability to variations in target scale. Finally, we replace the nearest-neighbor interpolation in the upsampling section with the lightweight operator CARAFE, effectively extracting parking space feature information and enhancing the algorithm’s feature fusion capabilities. Through ablation experiments and comparative trials on publicly available parking space datasets, our improved YOLOv7 algorithm achieves an mAP of 78.7%. Compared to the original algorithm, it demonstrates a 1.5% improvement in detection performance and a 5.6% increase in recall rate. These enhancements significantly improve parking space detection in complex environments, addressing issues such as false positives and false negatives, thereby meeting the performance requirements of parking space detection in parking lots.

Accurate detection of microalgae in ship ballast water: An innovative computer vision strategy

Microalgae play a crucial role in marine ecological preservation, especially in the management of ship ballast water, where accurate microalgae detection is essential for preventing biological invasions. However, when extracting and observing microalgae from ship ballast water samples, the intricate water quality background and cell adhesion phenomena present substantial interferences for precise microalgae detection, posing significant challenges for existing microalgae detection methods. To address these issues, this paper proposes an innovative microalgae detection method based on efficient omnidimensional dynamic convolution (EOD) and adhesion loss. The novelty of our study lies in the unique loss strategy for detecting adhesive microalgae cells for the first time and its combination with EOD to address the challenges of microalgae detection in complex environments. First, a large number of microalgae images are generated using an enhanced StyleGAN3, supplementing the dataset in complex scenarios to improve the model's robustness. Second, the proposed EOD is applied to mitigate the interference from the complex background and enhance the capability of microalgae feature extraction. Finally, our newly designed adhesion loss strategy imposes penalties on adhered cells based on intersection over union (IOU) and Gaussian distance, effectively tackling the cell adhesion issue. The experimental results indicate that this method achieves a mAP@[0.5:0.95] of 82.1% for microalgae detection in complex environments, meeting real-time microalgae detection requirements. This study provides an innovative perspective for marine ecological preservation, while its highly accurate and real-time microalgae detection method also pioneers new possibilities in microalgae detection research.

A 3,5-dinitropyridin-2yl Substituted Flavonol-based Fluorescent Probe for Rapid Detection of H2S in Water, Foodstuff Samples and Living Cells.

A novel flavonol-based fluorescent probe, Fla-DNT, has been synthesized for the rapid and specific detection of H2S. Fla-DNT exhibits excellent selectivity and anti-interference properties, a short response time (4min), large Stokes shift (138nm), and low detection limit (1.357 µM). Upon exposure to H2S, Fla-DNT displays a remarkable increase in fluorescence intensity at 542nm. Meanwhile, the recognizing site of H2S was predicted through Electrostatic potential and ADCH charges calculations, while the sensing mechanism of H2S was determined via HRMS analysis and DFT calculation. More importantly, the probe owes multiple applications, such as a recovery rate ranging from 92.00 to 102.10% for detecting H2S in water samples, and it can be fabricated into fluorescent strips to track H2S production during food spoilage by tracking color changes, thereby enabling real-time monitoring of food freshness. The bioimaging experiments demonstrate the capability of Fla-DNT to detect both endogenous and exogenous H2S in living cells. These results provide a reliable method and idea for H2S detection in complex environments.