Learning to Reduce Information Bottleneck for Object Detection in Aerial Images

Object detection in aerial images is a fundamental research topic in the domain of geoscience and remote sensing. However, advanced progresses on this topic are mainly focused on the designment of backbone networks or header networks, but surprisingly ignored the neck ones. In this letter, we first analyse the importance of the neck network in object detection frameworks from the theory of information bottleneck. Then, to alleviate the information loss problem in the current neck network, we propose a global semantic network, which acts as a bridge from the backbone to the head network in a bidirectional global convolution manner. Compared to the existing neck networks, our method has advantages of capturing rich detailed information and less computational costs. Moreover, we further propose a fusion refinement module, which is used for feature fusion with rich details from different scales. To demonstrate the effectiveness and efficiency of our method, experiments are carried out on two challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy and computational complexity both can verify the superiority of our method.Comment: 5 pages, 3 figure

Training-Free Instance Segmentation from Semantic Image Segmentation Masks

In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods (i.e., with image-level class labels or point labels) struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm for instance segmentation called training-free instance segmentation (TFISeg), which achieves instance segmentation results from image masks predicted using off-the-shelf semantic segmentation models. TFISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient. Specifically, we first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets and representative semantic segmentation baselines (including CNNs and Transformers) demonstrate that TFISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs. The code is available at: TFISegComment: 14 pages,5 figure

A Sandwich Electrochemical Immunosensor Using Magnetic DNA Nanoprobes for Carcinoembryonic Antigen

A novel magnetic nanoparticle-based electrochemical immunoassay of carcinoembryonic antigen (CEA) was designed as a model using CEA antibody-functionalized magnetic beads [DNA/Fe3O4/ZrO2; Fe3O4 (core)/ZrO2 (shell) nano particles (ZMPs)] as immunosensing probes. To design the immunoassay, the CEA antibody and O-phenylenediamine (OPD) were initially immobilized on a chitosan/nano gold composite membrane on a glassy carbon electrode (GCE/CS-nano Au), which was used for CEA recognition. Then, horseradish peroxidase (HRP)-labeled anti-CEA antibodies (HRP-CEA Ab2) were bound to the surface of the synthesized magnetic ZMP nanoparticles as signal tag. Thus, the sandwich-type immune complex could be formed between secondary antibody (Ab2) modified DNA/ZMPs nanochains tagged by HRP and GCE/CS-nano Au. Unlike conventional nanoparticle-based electrochemical immunoassays, the recognition elements of this immunoassay included both electron mediators and enzyme labels, which obviously simplifies the electrochemical measurement process. The sandwich-type immunoassay format was used for online formation of the immunocomplex of CEA captured in the detection cell with an external magnet. The electrochemical signals derived from HRP during the reduction of H2O2 with OPD as electron mediator were measured. The method displayed a high sensitivity for CEA detection in the range of 0.008–200 ng/mL, with a detection limit of 5 pg/mL (estimated at a signal-to-noise ratio of 3). The precision, reproducibility, and stability of the immunoassay were good. The use of the assay was evaluated with clinical serum samples, and the results were in excellent accordance with those obtained using the standard enzyme-linked immunosorbent assay (ELISA) method. Thus, the magnetic nanoparticle-based assay format is a promising approach for clinical applications, and it could be further developed for the detection of other biomarkers in cancer diagnosis

Comparison of Different Height–Diameter Modelling Techniques for Prediction of Site Productivity in Natural Uneven-Aged Pure Stands

Reliable estimates of forest site productivity are a central element of forest management. The model of height-diameter relationship of dominant trees using algebraic difference approach (ADA) is a commonly used method to measure site productivity of natural uneven-aged stands. However, the existing models of this method do not recognize site type or sample plot specific variability in height curves; thus, it cannot be effectively used to estimate site type or sample plot-related site productivity for natural uneven-aged stands. Two primary subject-specific approaches, ADA with dummy variable (DV) (ADA + DV) and ADA with combination of dummy variable and nonlinear mixed-effects modelling (CM) (ADA + CM), were proposed for height–diameter modelling. Height–diameter models developed with ADA, ADA + DV and ADA + CM were compared using data from 4161 observations on 349 permanent sample plots of four major natural uneven-aged pure stands (Spruce, Korean Larch, Mongolian Oak, and White Birch) in northeastern China. It was found that models developed with ADA + CM provided the best performance, followed by the models with ADA + DV, and the models developed with ADA performed the worst. Random effects at the plot level were substantial, and their inclusion greatly improved the model’s accuracy. More importantly, the models developed with ADA + CM provide an effective method for quantifying site type- and sample plot-specific forest site productivity for uneven-aged pure stands

Object Detection by Channel and Spatial Exchange for Multimodal Remote Sensing Imagery

Smart satellites and unmanned aerial vehicles (UAVs) are typically equipped with visible light and infrared (IR) spectrum sensors. However, achieving real-time object detection utilizing these multimodal data on such resource-limited devices is a challenging task. This article proposes HyperYOLO, a real-time lightweight object detection framework for multimodal remote sensing images. First, we propose a lightweight multimodal fusion module named channel and spatial exchange (CSE) to effectively extract complementary information from different modalities. The CSE module consists of two stages: channel exchange and spatial exchange. Channel exchange achieves global fusion by learning global weights to better utilize cross-channel information correlation, while spatial exchange captures details by considering spatial relationships to calibrate local fusion. Second, we propose an effective auxiliary branch module based on the feature pyramid network for super resolution (FPNSR) to enhance the framework's responsiveness to small objects by learning high-quality feature representations. Moreover, we embed a coordinate attention mechanism to assist our network in precisely localizing and attending to the objects of interest. The experimental results show that on the VEDAI remote sensing dataset, HyperYOLO achieves a 76.72% mAP50, surpassing the SOTA SuperYOLO by 1.63%. Meanwhile, the parameter size and GFLOPs of HyperYOLO are about 1.34 million (28%) and 3.97 (22%) less than SuperYOLO, respectively. In addition, HyperYOLO has a file size of only 7.3 MB after the removal of the auxiliary FPNSR branch, which makes it easier to deploy on these resource-constrained devices

Research on the Corrosion Fatigue Property of 2524-T3 Aluminum Alloy

The 2524-T3 aluminum alloy was subjected to fatigue tests under the conditions of R = 0, 3.5% NaCl corrosion solution, and the loading cycles of 106, and the S-N curve was obtained. The horizontal fatigue limit was 169 MPa, which is slightly higher than the longitudinal fatigue limit of 163 MPa. In addition, detailed microstructural analysis of the micro-morphological fatigue failure features was carried out. The influence mechanism of corrosion on the fatigue crack propagation of 2524-T3 aluminum alloy was discussed. The fatigue source characterized by cleavage and fracture mainly comes from corrosion pits, whose expansion direction is perpendicular to the principal stress direction. The stable propagation zone is characterized by strip fractures. The main feature of the fracture in the fracture zone is equiaxed dimples. The larger dimples are mixed with second-phase particles ranging in size from 1 to 5 μm. There is almost a one-to-one correspondence between the dimples and the second-phase particles. The fracture mechanism of 2524 alloy at this stage is transformed into a micro-holes connection mechanism, and the nucleation of micropores is mainly derived from the second-phase particles

Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach

Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements

Adsorption and Regeneration Properties of Tyrosine-Imprinted Polymeric Beads

Tyrosine-imprinted polymeric beads with diameters in the range 120–140 mm were prepared in an aqueous system by seed swelling and suspension polymerization using trimethylolpropane trimethacrylate (TRIM) as the crosslinking agent and acrylamide (AM), 2-vinylpyridine (VP) and/or 2-acrylamido-2-methylpropane sulphonic acid (AMPS), respectively, as functional monomers (FMs). The molecular recognition properties, dynamic adsorption behaviours and regeneration capabilities of the molecularly imprinted beads (MIBs) were investigated via a solid-phase extraction method. The functional monomers were found to be indispensable for preparing MIBs with highly specific molecular recognition properties. Irrespective of the choice of FM, when the FM/TRIM molar ratio was 4:15 the MIBs prepared exhibited the most desirable properties for the purposes of this study. When only one FM was used, it was found that AMPS provided the best solution as far as all the adsorption, recognition and regeneration capabilities of the MIBs were concerned. However, MIBs with the best molecular recognition and adsorption properties, and with only a slight sacrifice in their regeneration properties, were prepared using AMPS and VP as bifunctional monomers. For such MIBs, the initial separation factor, α, and static distribution coefficient, K D , were 2.01 and 2.15 ml/g, respectively

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management

A Hybrid Approach of Combining Random Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based on UAV RGB Data

Desert vegetation is an important part of arid and semi-arid areas, which plays an important role in preventing wind and fixing sand, conserving water and soil, maintaining the balanced ecosystem. Therefore, mapping the vegetation accurately is necessary to conserve rare desert plants in the fragile ecosystems that are easily damaged and slow to recover. In mapping desert vegetation, there are some weaknesses by using traditional digital classification algorithms from high resolution data. The traditional approach is to use spectral features alone, without spatial information. With the rapid development of drones, cost-effective visible light data is easily available, and the data would be non-spectral but with spatial information. In this study, a method of mapping the desert rare vegetation was developed based on the pixel classifiers and use of Random Forest (RF) algorithm with the feature of VDVI and texture. The results indicated the accuracy of mapping the desert rare vegetation were different with different methods and the accuracy of the method proposed was higher than the traditional method. The most commonly used decision rule in the traditional method, named Maximum Likelihood classifier, produced overall accuracy (76.69%). The inclusion of texture and VDVI features with RGB (Red Green Blue) data could increase the separability, thus improved the precision. The overall accuracy could be up to 84.19%, and the Kappa index with 79.96%. From the perspective of features, VDVI is less important than texture features. The texture features appeared more important than spectral features in desert vegetation mapping. The RF method with the RGB+VDVI+TEXTURE would be better method for desert vegetation mapping compared with the common method. This study is the first attempt of classifying the desert vegetation based on the RGB data, which will help to inform management and conservation of Ulan Buh desert vegetation