Infrared small target segmentation networks: A survey

Efficiently and accurately separating infrared (IR) small targets from complex backgrounds presents a significant challenge. Numerous studies in the literature have proposed various feature fusion modules designed specifically to enhance the extraction of IR small target features. While these designs offer some incremental improvement to the accuracy of IR small target detection, they come at a steep cost of significantly increasing network parameters and FLOPs. Striving for a balance between computational efficiency and model accuracy, we decided to forgo these complex feature fusion modules. Instead, we developed a new lightweight encoding and decoding structure known as the Lightweight IR Small Target Segmentation Network (LW-IRSTNet). This structure integrates regular convolutions, depthwise separable convolutions, atrous convolutions, and asymmetric convolutions modules. In addition, we devised post-processing modules including an eight-neighborhood clustering algorithm and an online target feature adjustment strategy. Experimental results indicate that: 1) the segmentation accuracy metrics of LW-IRSTNet match the best results of 14 state-of-the-art comparative baselines; 2) the parameters and FLOPs of LW-IRSTNet, at only 0.16M and 303M respectively, are significantly smaller in comparison to these baselines; and 3) the post-processing modules enhance both user-friendliness and the robustness of algorithm deployment. Moreover, LW-IRSTNet has been successfully implemented on both embedded platforms and websites, expanding its range of applications. Utilizing the ONNX framework, NPU acceleration, and CPU multi-threaded resource allocation, we have been able to achieve high-performance inference capabilities, as well as online dynamic threshold adjustment with the LW-IRSTNet. The source codes for this project can be accessed at https://github.com/kourenke/LW-IRSTNet. 

LW-IRSTNet: Lightweight Infrared Small Target Segmentation Network and Application Deployment

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack. However, IR small targets are very difficult to detect because of their weak brightness, small size, and lack of shape, structure, texture, and other information elements. In order to simultaneously satisfy the robustness and timeliness of target detection, inspired by density peak clustering and the human visual system, an idea combining an improved density peak global search and local contrast calculation is proposed. First, the positions of candidate targets are determined in the preprocessed image using the improved density peak global search method (IDPGSM). Second, the saliency map is obtained using the double-weights enhanced local contrast method (DWELCM) for the candidate target neighborhood. Finally, adaptive threshold segmentation is used to detect IR small targets. Through comprehensive analysis of five evaluation indicators, the experimental results on seven real sequences and three hundred IR images of different scenes that the proposed method has better detection performance compared with six baseline methods. It can quickly and accurately determine the small target position in the case of severe background clutter and noise interference.

/pdf/infrared-small-target-detection-based-on-the-improved-gzef9rze.pdf

Infrared Small Target Detection Based on the Improved Density Peak Global Search and Human Visual Local Contrast Mechanism

To effectively evaluate the detection capability of the infrared search and tracking system (IRST), the mathematical models of target infrared (IR) radiation intensity envelope, atmospheric transmittance, and signal-to-noise ratio (SNR) operating range are improved, respectively. Based on the above three models, the IRST performance evaluation simulation system is designed to analyze the optimal detection point and effective detection area under different conditions (such as target speed, detection angle, detection probability, false alarm probability, and operating range). Meanwhile, in order to verify the validity of the model, the range evaluation method of calibration and cross-validation is proposed. And taking the cross-validation method as the baseline, the error of the optimized mathematical model of IRST in this paper is within 10%. The research results are of reference significance for IRST operating range evaluation, design, and use.

Detection model and performance evaluation for the infrared search and tracking system.

The implementation of Clear Aligner Therapy (CAT) in adult orthodontics exemplifies the integration of advanced technology in the dental healthcare sector. Representing a significant shift in modern orthodontics, CAT offers a convenient and aesthetic alternative to traditional fixed appliance treatments for mal-aligned dentition. This narrative review aims to explore the applicability of CAT, delineating its biomechanics, indications, contraindications, scope, limitations, and factors influencing long-term stability and successful outcomes. A comprehensive literature search was conducted using databases like Google Scholar, PubMed, Cereus, and the Cochrane Library. Articles were selected based on their relevance to clear aligners, without brand specificity, and covered a wide range of cases to establish CAT’s scope and limitations. This review includes individual case studies, systemic reviews, comparative analyses, case reports, finite element analyses, and prospective and retrospective analyses, all contributing to a nuanced understanding of CAT’s applicability and long-term treatment stability. The conclusion underscores CAT’s growing acceptance in orthodontics, including its application in challenging cases, and highlights key determinants that bolster its long-term efficacy.

Stability and Success of Clear Aligners in Orthodontics: A Narrative Review

The single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems in terms of measurement speed and system cost. This system necessitates a robust spatial phase unwrapping algorithm. However, robust spatial phase unwrapping remains a challenge in complex scenes. Quality-guided spatial phase unwrapping needs more efficient ways to identify unreliable points in phase maps before unwrapping. End-to-end deep learning spatial phase unwrapping face generality and interpretability problems. This paper proposes a hybrid spatial phase unwrapping method combining deep learning-enabled invalid-point removal and traditional path-following. This hybrid method demonstrates better robustness than traditional quality-guided methods, better interpretability than end-to-end deep learning schemes, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, fringe number, fringe direction, and optics wavelength verify the effectiveness of the proposed method. 

Deep learning-enabled invalid-point removal for spatial phase unwrapping of 3D measurement

OBJECTIVES
To compare mandibular relative anchorage loss (RAL) under reciprocal anchorage between first and second premolar extraction cases in bimaxillary protrusion mild crowding cases treated using clear aligner therapy (CAT).


MATERIALS AND METHODS
Adult patients who met the following criteria were included: treated using CAT with bilateral mandibular premolar extractions and space closure using intra-arch reciprocal anchorage. RAL was defined as the percent molar mesial movement relative to the sum of molar mesial plus canine distal movement. Movements of the mandibular central incisor (L1), canine (L3), and first molar (L6) were measured based on superimposition of the pre- and post-treatment dentition and jaw models.


RESULTS
Among the 60 mandibular extraction quadrants, 38 had lower first premolar (L4) and 22 had lower second premolar (L5) extracted. L6 mesial movement was 2.01 ± 1.11 mm with RAL of 25% in the L4 extraction group vs 3.25 ± 1.19 mm with RAL of 40% in the L5 extraction group (P < .001). Tooth movement efficacy was 43% for L1 occlusogingival movement, 75% for L1 buccolingual inclination, 60% for L3 occlusogingival movement, and 53% for L3 mesiodistal angulation. L1 had unwanted extrusion and lingual crown torquing whereas L3 had unwanted extrusion and distal crown tipping, on which the power ridges or attachments had little preventive effect.


CONCLUSIONS
The average mandibular reciprocal RAL is 25% or 40% for extraction of L4 or L5, respectively, in CAT cases. A RAL-based treatment planning workflow is proposed for CAT extraction cases.

/pdf/relative-anchorage-loss-under-reciprocal-anchorage-in-3va0o086.pdf

Relative anchorage loss under reciprocal anchorage in mandibular premolar extraction cases treated with clear aligners.

To improve the recognition probability of airborne infrared search and track systems (IRSTS) in air-to-air operations, the point target recognition probability model is established. And using an operating range model, the detection envelope is calculated for the airborne IRSTS. Combining with different operational backgrounds and specific technical parameters, the simulation results show that: (1) an increase in visual range and target speed, along with a decrease of oblique angle, produces an increase in the recognition probability; (2) the angle of maximum operating range of each detection envelope is the optimum detection point for the airborne IRSTS; and (3) the optimum threshold noise ratio is 8-10. These conclusions provide further scope in improving the operational effectiveness of airborne IRSTS in practical applications.

Optimum selection of detection point and threshold noise ratio of airborne infrared search and track systems.

In terms of 3D imaging speed and system cost, the single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems. This system necessitates a robust spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a challenge in complex scenes. Quality-guided SPU algorithms need more efficient ways to identify the unreliable points in phase maps before unwrapping. End-to-end deep learning SPU methods face generality and interpretability problems. This paper proposes a hybrid method combining deep learning and traditional path-following for robust SPU in FPP. This hybrid SPU scheme demonstrates better robustness than traditional quality-guided SPU methods, better interpretability than end-to-end deep learning scheme, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, the number of fringes, fringe direction, and optics wavelength verify the effectiveness of the proposed method.

/pdf/deep-learning-enabled-spatial-phase-unwrapping-for-3d-1qde3mu7.pdf

Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement

Modulation of phase preference of single units in awake non-human primate using tACS

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