ILNet: Low-level Matters for Salient Infrared Small Target Detection

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22% nIoU and 1.33e-6 Fa) and IRSTD-1K (68.91% nIoU and 3.23e-6 Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://github.com/Li-Haoqing/ILNet

Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection

Infrared Small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotation. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field. This letter proposed a labor-efficient and cursory annotation framework with level set, which obtains a high-quality pseudo mask with only one cursory click. A variational level set formulation with an expectation difference energy functional is designed, in which the zero level contour is intrinsically maintained during the level set evolution. It solves the issue that zero level contour disappearing due to small target size and excessive regularization. Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach achieves superior performance. Code is available at https://github.com/Li-Haoqing/COM.Comment: 4 pages, 5 figures, references adde

Restoration of horizontal stability in complete acromioclavicular joint separations: surgical technique and preliminary results

BACKGROUND: Our purpose was to investigate the clinical efficacy of arthroscope-assisted acromioclavicular ligament reconstruction in combination with double endobutton coracoclavicular ligament reconstruction for the treatment of complete acromioclavicular joint dislocation. METHODS: During the period from February 2010 to October 2012, ten patients with Rockwood types IV and V acromioclavicular joint dislocation were hospitalized and nine were treated with acromioclavicular ligament reconstruction combined with double endobutton of coracoclavicular ligament reconstruction. The improvement in shoulder functions was assessed using a Constant score and visual analog scale (VAS) system. RESULTS: The mean follow-up period was 33.6 ± 5.4 months. The mean Constant scores improved from 25.2 ± 6.6 preoperatively to 92.4 ± 6.5 postoperatively, while the mean VAS score decreased from 5.9 ± 1.4 to 1.2 ± 0.9; significant differences were observed. The final follow-up revealed that excellent outcomes were achieved in eight patients and good outcome in two patients. CONCLUSION: Arthroscope-assisted acromioclavicular ligament reconstruction in combination with double endobutton of coracoclavicular ligament reconstruction is an effective approach for treatment of acute complete acromioclavicular joint dislocation

Verse: A Python library for reasoning about multi-agent hybrid system scenarios

We present the Verse library with the aim of making hybrid system verification more usable for multi-agent scenarios. In Verse, decision making agents move in a map and interact with each other through sensors. The decision logic for each agent is written in a subset of Python and the continuous dynamics is given by a black-box simulator. Multiple agents can be instantiated and they can be ported to different maps for creating scenarios. Verse provides functions for simulating and verifying such scenarios using existing reachability analysis algorithms. We illustrate several capabilities and use cases of the library with heterogeneous agents, incremental verification, different sensor models, and the flexibility of plugging in different subroutines for post computations.Comment: 26 pages, 16 figure

Recursive classification of satellite imaging time-series: An application to water mapping, land cover classification and deforestation detection

A wide variety of applications of fundamental importance for security, environmental protection and urban development need access to accurate land cover monitoring and water mapping, for which the analysis of optical remote sensing imagery is key. Classification of time-series images, particularly with recursive methods, is of increasing interest in the current literature. Nevertheless, existing recursive approaches typically require large amounts of training data. This paper introduces a recursive classification framework that improves the decision-making process in multitemporal and multispectral land cover classification algorithms while requiring low computational cost and minimal supervision. The proposed approach allows the conversion of an instantaneous classifier into a recursive Bayesian classifier by using a probabilistic framework that is robust to non-informative image variations. Three experiments are conducted using Sentinel-2 data. The first one consists in the water mapping of an embankment dam in California (United States), the second one is a land cover classification experiment of the Charles river area in Boston (United States) and the last experiment addresses deforestation detection in the Amazon rainforest (Brazil). A classifier based on the Gaussian mixture model (GMM), a logistic regression (LR) classifier, and a spectral index classifier (SIC) are compared to their recursive counterparts. SICs are introduced to convert the NDWI, MNDWI and NDVI spectral indices into predictive probabilities. Two state-of-the-art deep learning-based models are also used as a benchmark for the water mapping experiment. Results show that the proposed method significantly increases the robustness of existing instantaneous classifiers in multitemporal settings. Our method also improves the performance of deep learning-based classifiers without the need for additional training data.Comment: Without supplementary results: 30 pages, 11 figures. With supplementary results: 40 pages, 21 figure

Robust Variational-based Kalman Filter for Outlier Rejection with Correlated Measurements

State estimation is a fundamental task in many engineering fields, and therefore robust nonlinear filtering techniques able to cope with misspecified, uncertain and/or corrupted models must be designed for real-life applicability. In this contribution we explore nonlinear Gaussian filtering problems where measurements may be corrupted by outliers,and propose a new robust variational-based filtering methodology able to detect and mitigate their impact. This method generalizes previous contributions to the case of multiple outlier indicators for both independent and dependent observation models. An illustrative example is provided to support the discussion and show the performance improvement

Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?

Navigation problems are generally solved applying least-squares (LS) adjustments. Techniques based on LS can be shown to perform optimally when the system noise is Gaussian distributed and the parametric model is accurately known. Unfortunately, real world problems usually contain unexpectedly large errors, so-called outliers, that violate the noise model assumption, leading to a spoiled solution estimation. In this work, the framework of robust statistics is explored to provide robust solutions to the global navigation satellite systems (GNSS) single point positioning (SPP) problem. Considering that GNSS observables may be contaminated by erroneous measurements, we survey the most popular approaches for robust regression (M-, S-, and MM-estimators) and how they can be adapted into a general methodology for robust GNSS positioning. We provide both theoretical insights and validation over experimental datasets, which serves in discussing the robust methods in detail

Improving yield and water use efficiency of apple trees through intercrop-mulch of crown vetch (Coronilla varia L.) combined with different fertilizer treatments in the Loess Plateau

Improving water use efficiency (WUE) and soil fertility is relevant for apple production in drylands. The effects of intercrop-mulch (IM) of crown vetch (Coronilla varia L.) combined with different fertilizer treatments on WUE of apple trees and soil fertility of apple orchards were assessed over three years (2011, 2013 and 2014). A split-plot design was adopted, in which the main treatments were IM and no intercrop-mulch (NIM). Five sub-treatments were established: no fertilization (CK); nitrogen and phosphorus fertilizer (NP); manure (M); N, P and potassium fertilizer (NPK); and NPK fertilizer combined with manure (NPKM). Due to mowing and mulching each month during July–September, the evapotranspiration for IM was 17.3% lower than that of NIM in the dry year of 2013. Additionally, the soil water storage of NPKM treatment was higher than that of CK during the experimental period. Thus, single fruit weight and fruit number per tree increased with IM and NPKM application. Moreover, applying NPKM with IM resulted in the highest yield (on average of three years), which was 73.25% and 130.51% greater than that of CK in IM and NIM, respectively. The WUE of NPKM combined with IM was also the highest in 2013 and 2014 (47.69 and 56.95% greater than applying IM alone). In addition, due to application of IM combined with NPKM, soil organic matter was increased by 25.8% compared with that of CK (in NIM). Additionally, application of IM combined with NPKM obtained more economic net return, compared to other combinations. Therefore, applying NPKM with IM is recommended for improving apple production in this rain-fed agricultural area

Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness