Light Camera Research Articles

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies.

A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens' health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming.

Motion-free high-resolution on-chip microscopy using LED matrix

Lensless microscopy is an imaging technique that allows high-resolution imaging over a large field of view with a cost-effective design. Conventional lensless microscopy often utilizes multi-height phase retrieval and pixel-super-resolution algorithms to reconstruct high-resolution images, requiring mechanical stages for three-dimensional relative movements between a light source, camera, and sample. However, the excessive use of stages inevitably increases the bulkiness of the system and extends the image acquisition time. Here, we propose a motion-free lensless microscope that incorporates an RGB LED matrix array. A high-resolution holographic image is reconstructed from subpixel-shifted color images obtained with LED illuminations without any mechanical movement. Using a prototype system, we have demonstrated a spatial-bandwidth product of 30 megapixels with a resolution of 0.87 µm and a field of view of 24 mm2. The usability of the proposed method has been further tested for histopathologic examination. Our system features a compact and high-performance design with inexpensive optoelectronic elements, a conventional CMOS sensor and an LED matrix, which are well-aligned with the original design motivation of lensless imaging methods.

Hypergolic fuel impacting a gelled oxidizer wall: Droplet dynamics, heat release, ignition, and flame analysis

The combination of high-concentration solutions of hydrogen peroxide, known as High Test Peroxide (HTP), with the green fuel composed of tetramethylethylenediamine, dimethylaminoethanol, and methanol (TMEDA/DMEA/MeOH, 1:1:1 vol%), catalyzed by 1 wt% of copper chloride dihydrate, has significant potential for space hypergolic propulsion applications in terms of performance and safe operation. Besides the well-known and mature propulsion systems adopting liquid and solid propellants, gel propellants also present interesting characteristics that can be better explored to enable the creation of alternative propulsive systems. The present work employed HTP at 98 wt% with 6 wt% of fumed silica as a gelling agent to create gelled HTP (GHTP). Droplets of the green fuel impinged on a layer of GHTP placed over a glass slide, acting as a solid oxidizer, mimicked an element of a new hybrid gel/liquid hypergolic propulsion system. Data from infrared and visible light cameras enabled a detailed analysis of the dynamics of a reactive droplet impinging on a gelled oxidizer wall, as well as the heat release, ignition, and flame spread of the hypergolic GHTP/green fuel pair under open-air conditions. The heat release was observed to be distributed across concentric annular areas for both the fuel surrogate and the fuel with catalyst, before and after ignition, demonstrating a strong correlation with non-reactive droplet fluid dynamics, which was corroborated by spread diameter analysis processed from visible image data. Vapor and Ignition Delay Times (VDT and IDT) were found to be as low as 4 and 17 ms, respectively, for the highest droplet impact velocity and catalyst concentration. The reaction rate in the gas phase, considering the flame spread area, showed a dependency of both impact velocity and catalyst concentration, with the latter exhibiting a more pronounced and clear effect. The surface temperature ranges where first vaporization and ignition occurred were 60 °C ∼ 70 °C and 120 ∼ 170 °C, respectively, which is close to the boiling point of methanol and the auto-ignition temperature of TMEDA. This finding, along with the chemical mechanisms in the gas phase related to the presence of a catalyst in the fuel, may be important for hypergolic ignition. The relatively low ignition temperatures and the short ignition times represent additional advantages of the present hypergolic combination for propulsion applications.

Cross-Modal Face Super-Resolution Based on Quasi-Siamese Domain Transfer Fusion Network

In this paper, we propose a Cross-Modal Face Super-Resolution (CMFSR) method to construct high-resolution (HR) facial images from low-resolution (LR) cross-modal facial images captured respectively by disjoint visible light (VIS) and near-infrared (NIR) cameras. Due to the coupling of modality transformation and information fusion, CMFSR is more difficult to obtain HR reconstructed results compared with traditional super-resolution. To solve this problem, a Quasi-Siamese Domain Transfer Fusion Network (QSDTFN) for CMFSR is proposed in this paper, whose two branches transfer two LR face modality to HR face modality by domain transfer respectively. Different from two completely independent branches in the traditional pseudo-siamese network, only the HR-to-LR face transfer processes of the two branches in our quasi-siamese network are independent, while the LR-to-HR face transfer processes are coupled. This coupled module called the Adaptive Weighted Domain Transfer Fusion Module (AWDTFM) disentangles the modality and identity information in the two LR faces, thus achieving modality transformation and identity information fusion simultaneously. In order to strengthen the optimization on the process of CMFSR, this method further introduces the backward QSDTFN to form a higher-level bidirectional structure with the forward QSDTFN, and specifically designs two types of losses: intra-network loss and inter-network loss, to constrain the modality and identity consistencies within one QSDTFN and between two QSDTFNs respectively. The experimental results on the challenging LR cross-modal face datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.

The shading isophotes: Model and methods for Lambertian planes and a point light

Structure-from-Motion (SfM) and Shape-from-Shading (SfS) are complementary classical approaches to 3D vision. Broadly speaking, SfM exploits geometric primitives from textured surfaces and SfS exploits pixel intensity from the shading image. We propose an approach that exploits virtual geometric primitives extracted from the shading image, namely the level-sets, which we name shading isophotes. Our approach thus combines the strength of geometric reasoning with the rich shading information. We focus on the case of untextured Lambertian planes of unknown albedo lit by an unknown Point Light Source (PLS) of unknown intensity. We derive a comprehensive geometric model showing that the unknown scene parameters are in general all recoverable from a single image of at least two planes. We propose computational methods to detect the isophotes, to reconstruct the scene parameters in closed-form and to refine the results densely using pixel intensity. Our methods thus estimate light source, plane pose and camera pose parameters for untextured planes, which cannot be achieved by the existing approaches. We evaluate our model and methods on synthetic and real images.

Spatial frequency modulation imaging for laser processing applications

Single element detection, spatial frequency modulation imaging (SPIFI) is deployed in a laser processing environment. SPIFI images are used to monitor a laser melting process, like that encountered during additive manufacturing, with an exposure time of 120μs, which enables real-time, in-situ monitoring of melt track formation coaxial with the processing laser. SPIFI images from a single photodiode are shown to be comparable to or better than white light camera images taken at the same numerical aperture. SPIFI deployed in this manner represents a disruptive new sensor metrology system that, when coupled with developing process models, can ultimately be used to validate statistically significant process parameter-signature-quality relationships with quantified uncertainty.

Design and experiment of a stereoscopic vision-based system for seeding depth consistency adjustment

The basic process of corn sowing includes seed selection, land preparation, fertilization, sowing, and soil compaction. Soil compaction is an important step in the sowing process, playing a crucial role in protecting the seeds, promoting germination and root development, and providing a stable growth environment for corn. Currently, mainstream soil compaction devices used in corn sowing employ non-active adjustment structures, which cannot regulate the amount of soil covering and the compaction force for individual seeds during the sowing process, making it difficult to ensure consistent sowing depth. To address these issues, this study investigates the soil compaction device on a corn planter and proposes a soil compaction device that utilizes a binocular structured light camera to detect the opening depth of the planter and flexibly adjust the soil covering and compaction force for each seed. Experimental evaluations of the device’s performance were also conducted. The design of the sowing depth consistency control system includes the selection and application of the design, motor, gearbox, binocular structured light camera, dust removal device, user interface, electric-driven soil compaction device, and control system. The experimental results showed that when the system detects a variation in trench depth of around 2 cm, the average response time of the system is 2.23 s with a standard deviation of 0.042 s. When the system detects a variation in trench depth of around 4 cm, the average response time of the system is 4.68 s with a standard deviation of 0.078 s. This suggests that the system’s response time fluctuates within 0.1 s, indicating good stability of the system. The average error of the planter’s opening depth, as measured by the binocular structured light camera, is approximately 6 mm, the success rate of detection can be maintained above 70 % under different trench depths. The dust removal device’s performance meets the requirements of the detection system. The research demonstrates that the sowing depth consistency control system developed in this study can accurately detect the planter’s opening depth during operation and adjust the soil covering, compaction force appropriately based on the depth information provided by the soil compaction device.

A system for high-speed NIR transmission imaging of drug delivery in tissue

The development and characterisation of new drug delivery techniques often requires visualising the location of delivered fluid in tissue samples. The opacity of tissue means that this can typically only be done with destructive post-processing techniques, or with the use of ionising radiation (eg. X-ray). In this work we present a novel approach to imaging fluid during delivery into port-mortem tissue samples using transmission imaging at near-infrared (NIR) wavelengths. The system makes use of wavelengths just above the visible range (765 nm), at which tissue is relatively transparent, and to which visible light cameras remain sensitive. Thus, this system utilised a low-cost high-speed (1000 fps) camera and optical components designed for use with visible light. We demonstrate that this NIR imaging system is capable of measuring the location of delivered fluid and an 18G needle in 12 mm thick samples of muscle and fat tissue at 1000 fps. Predictions of needle depth using the imaging system compared well to the known needle position in the tissue: the standard deviation of the depth measurements was approximately 0.7 mm in muscle and 1.2 mm in fat. NIR images captured during and after delivery compare favourably with observations made using 2D X-ray and by sectioning the tissue post-injection. This system can support studies of fluid dispersion and penetration into tissue by new drug delivery systems, thus assist the development of new drug delivery techniques.

Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT

Robust light transport algorithms, particularly bidirectional path tracing (BDPT), face significant challenges when dealing with specular or highly glossy involved paths. BDPT constructs the full path by connecting sub-paths traced individually from the light source and camera. However, it remains difficult to sample by connecting vertices on specular and glossy surfaces with narrow-lobed BSDF, as it poses severe constraints on sampling in the feasible direction. To address this issue, we propose a novel approach, called proxy sampling , that enables efficient sub-path connection of these challenging paths. When a low-contribution specular/glossy connection occurs, we drop out the problematic neighboring vertex next to this specular/glossy vertex from the original path, then retrace an alternative sub-path as a proxy to complement this incomplete path. This newly constructed complete path ensures that the connection adheres to the constraint of the narrow lobe within the BSDF of the specular/glossy surface. Unbiased reciprocal estimation is the key to our method to obtain a probability density function (PDF) reciprocal to ensure unbiased rendering. We derive the reciprocal estimation method and provide an efficiency-optimized setting for efficient sampling and connection. Our method provides a robust tool for substituting problematic paths with favorable alternatives while ensuring unbiasedness. We validate this approach in the probabilistic connections BDPT for addressing specular-involved difficult paths. Experimental results have proved the effectiveness and efficiency of our approach, showcasing high-performance rendering capabilities across diverse settings.

On the Parameter Calibration Method for Dual-Spectral Triocular Camera

The dual-spectrum triocular camera system composed of a binocular visible light camera and mid-infrared camera is used for high-precision thermal fault detection and thermal field reconstruction of industrial equipment. The realization of its function depends on the high-precision camera parameter calibration. The difficulty lies in how to realize the infrared camera calibration quickly and improve the parameter accuracy of multi-lens camera. In this paper, according to the characteristics of the dual-spectral triocular camera system, the theoretical model is constructed, and the circular asymmetric calibration plate under infrared supplementary light is selected as the calibration object through experiments. A method for combining infrared image adaptive histogram enhancement and binarization processing based on the Sauvola algorithm is proposed to effectively calibrate the infrared camera. A global parameter optimization method based on traditional passive vision binocular stereo calibration is proposed. The optimization of experimental parameters finds the reprojection error value is 0.16, which meets the demand of high-precision calibration and solves the problem of difficult-to-calibrate weak image texture of infrared camera and the calibration accuracy of the camera under different imaging capabilities.

Road Traffic Enforcement: Using Technology Through Public-Private Partnerships (PPP)

The past two decades have seen a wide - ranging wave of privatizations in formerly government owned or - controlled activities. A government task or service which is funded and operated through a partnership between a public sector authority and private sector company based on guidelines and performance standards set by a public party is typically referred to as a Public Private Partnership (PPP). In most countries traffic enforcement cameras and other equipment are purchased, owned, and operated by government organisations. Back - offices, where violation data are processed, citations issued, and traffic fines collected, have traditionally been government - run activities. In a PPP model, a private party invests and installs enforcement equipment (e. g. speed or red - light cameras) and conducts back office processing, such as sending out violation notices and managing and confirming fine collection. The investment in equipment and related processing and support activities are funded by fine revenues collected from the motoring public over the contract term. Gaining public support, and ultimately acceptance, for the PPP model is crucial, which is why transparency, publicity and continuous communication are key factors for successful and sustainable implementation. Full service PPPs are often organised on a municipal level in close consultation with the local administration and police. Due to the high initial investment and gradual repayment, such PPPs are typically based on longer term contracts. Moreover, the regulatory environment may require some changes to allow involvement of a private party. One process that is difficult to outsource is the formal confirmation of a violation by a review of the photo or video evidence. Generally, this can only be validly performed by an authorised government official, such as a police officer. Depending on laws and regulations applicable to specific jurisdictions, there could be more issues that may limit the extent of the private party’s involvement in these PPPs. When properly implemented, PPP’s in traffic enforcement can considerably contribute to the reduction of casualties, injuries and crashes, as well as improve traffic flow, and increase quality of life with more safety, lower emissions and less noise pollution. Speed reduction also significantly benefits the safety of vulnerable road users such as pedestrians, and two - wheelers Personal injury and death on roadways also have a significant impact on the economy through medical costs, lost resources and wages and disability pay outs. These costs are significant and are estimated by the WHO to amount to an average of around 3% of a country’s GNP. This figure rises to 5% in some low - and middle - income countries

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Abstract Traditional vision-based inertial odometry (VIO) suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units (IMUs). First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error (RMSE) by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

Design of an integrated imaging system of airborne SAR and visible light camera based on common aperture antenna

In recent years, the advantages of multi-sensor integration are gradually highlighted. In this paper, an integrated imaging system (IIS) of airborne synthetic aperture radar (SAR) and visible light camera (VLC) based on common aperture antenna is designed to meet the mission requirement of acquiring visible light and SAR images simultaneously. The IIS adopts the coaxial scheme, its structure is simple and compact, and is more suitable for airborne platform. In this paper, a parameter design method of IIS based on common aperture antenna is proposed. After the system parameters design, the imaging performance of IIS is analyzed by simulation. The results show that the system parameters are designed reasonably and the imaging performance meets the mission requirement. The designed IIS can realize the visible and Ka-band (30 GHz) dual-band imaging. Under the condition of 20 km altitude, the imaging resolutions of VLC and SAR are higher than 0.2 m and 0.3 m respectively, the field of view of VLC is 1.46°, and the swath length of SAR is greater than 870 m.

Application of Independent Component Analysis and Nelder-Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation.

In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder-Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.

Calibration-free deep optics for depth estimation with precise simulation

Monocular depth estimation is an important computer vision task widely explored in fields such as autonomous driving, robotics, and more. Recently, deep optics approaches that optimize diffractive optical elements (DOEs) with differentiable frameworks have improved depth estimation performance. However, they only consider on-axis point spread functions (PSFs) and are highly dependent on system calibration. We propose a precise end-to-end paradigm combining ray tracing and angular spectrum diffraction. With this approach, we jointly trained a DOE and a reconstruction network for depth estimation and image restoration. Compared with conventional deep optics approaches, we accurately simulate both on-axis and off-axis PSFs, eliminating the need for calibration. We have validated the high similarity between captured PSFs and simulated ones at 19.4∘ field-of-view (FOV). Our optimized phase mask and network achieve state-of-the-art performance among semantic-based monocular depth estimation and existing deep optics methods. During real-world experiments, our prototype camera shows depth distributions similar to the Intel D455 infrared structured light camera.

Structured illumination with infrared imaging for measuring thermal conductivity

With developments in advanced manufacturing and materials by design comes the need for high-throughput thermal characterization and inspection. Towards this end, Structured Illumination with Thermal Imaging (SITI) is an all-optical pump-probe thermal characterization technique recently developed by our group. In the first generation [Zheng et al., Appl. Phys. Rev. 9, 021411 (2022)] SITI uses an LED with a digital micromirror device (DMD) to “structurally illuminate” and heat the sample with dynamic patterns, a visible light camera for thermoreflectance based “thermal imaging” [leveraging a Microsanj MTIR120], and the resultant temperature response was fit with a thermal model to characterize the sample’s thermal properties. This represents a novel approach to dynamic and flexible spatial mapping of thermal properties by virtue of being a non-contact technique and having a simpler scanning means (computer control only) than conventional pump-probe laser methods. SITI also can tolerate rough samples with diffuse reflections. This talk presents the second generation of SITI. The pumping is now based on a lower cost off-the-shelf digital projector. The thermometry is now performed using an infrared (IR) camera, which we find is a more flexible and accessible hardware approach compared to the thermoreflectance microscopy used previously. With these updates the setup can deliver higher heating power and a broader range of frequencies, allowing an extended range of samples that can be studied. We have demonstrated SITI’s ability to measure the thermal conductivity of a microscope glass slide.

Design of a Multimodal Detection System Tested on Tea Impurity Detection

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.

Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.