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A binocular camera composed of colour and depth cameras is an accurate and inexpensive tool for recognising fruit trees to plan spraying paths. For this purpose, however, the accurate visual registration of the depth and colour cameras is a problem that must be solved. This paper proposes a local feature point extraction (LFPE) algorithm for depth-colour image registration, which automatically extracts the feature points of local regions in the colour and depth images for registration. First, the binarised colour and depth images are divided into local regions after edge extraction, and the regions in which the edge features match at several pixels between images are selected. Then, these regions are again divided into smaller regions to detect local extrema of the pixels as feature points. Finally, the matching feature points are calculated based on the Euclidean distance and are used to optimise the affine transformation matrix. To verify the accuracy and effectiveness of the algorithm, it is compared with the FAST, SURF and BRISK algorithms. The experimental results show that the accuracy of the proposed LFPE algorithm is 1.6 times that of BRISK, 3.5 times that of FAST and 40.1 times that of SURF. Therefore, it is more suitable for registration between depth and colour images. • Proposed a depth-colour image registration method for orchard precision spraying. • A local feature point extraction (LFPE) algorithm is proposed to match images. • The registration accuracy of LFPE is much better than other similar algorithms. [ABSTRACT FROM AUTHOR]

• A small target detection algorithm based on feature interaction and guided learning. • A feature guided alignment module is designed to obtain more effective features. • Gaussian distribution sample allocation strategy is introduced to obtain better samples. • Feature interaction and decoupling detection enhance the detection task capability. • A new regression loss function is used to optimize the localization ability to a certain extent. At present, for the diverse target sizes, different angles, and complex environment in aerial images, how to detect small objects effectively in aerial images is still a challenge task. In order to further improve the detection accuracy and reduce the missing rate, in this paper, we proposes a small target detection algorithm based on feature interaction and guided learning. Firstly, the feature guided alignment module is designed to deal with the problem of information loss and overlap. Secondly, the misallocation of small object samples is optimized by introducing gaussian sample allocation strategy. Then the interactive parallel detection header is designed to solve the detection task conflict problem. Finally, the new regression loss function is designed to enhance the localization ability of the target. The experimental results on the Tinyperson dataset and Visdrone dataset show that this model can improve the detection accuracy and reduce the missing rate of small targets effectively. [ABSTRACT FROM AUTHOR]

The unavailability and variability of training samples are the two essential concerns in the training of deep neural network models for image classification. For automated image monitoring systems, these problems are difficult when training a model through supervised learning methods because of the time and effort required. This paper proposes an adaptive solution to this problem by applying online semi-supervised learning to an automated insect pest monitoring system. The method used includes unsupervised pseudo-labelling of insect images and the training of semi-supervised classifier models for insect image recognition. The pseudo-labelling algorithm includes three major components: image labelling, label reconfirmation, and sample cleaning. Experiments were conducted on two unlabelled 1-year insect image datasets to evaluate the efficacy of the proposed method. It was found that the pseudo-labelling algorithm could achieve accuracy up to 0.963, hence enabling automated training data collection. The temporal improvement of the insect recognition performance by including new training data to retrain the classifier model was comparable in performance to the supervised learning approach as evaluated by cluster density, silhouette score, and F 1 -score. The proposed method was also able to automatically collect quality samples and train models regardless of the complexity of the images, making it a good alternative to replace laborious supervised learning. The proposed method can prevent contamination of a training dataset when images from new locations are collected. The presented techniques may also be used in other continuous learning applications that require automated training data collection and online model update. [Display omitted] • Semi-supervised learning method for an insect pest monitoring system was proposed. • Accuracy of the pseudo-labelling algorithm can be as high as 0.963. • Characteristics and variation of training data in classifier models were assessed. • Performance of classifier models can be improved through semi-supervised learning. • Techniques can be used for automated training data collection and model update. [ABSTRACT FROM AUTHOR]

• Proposal of a methodology for estimating the period of faulty impulses. • Development of an APMESR algorithm for extracting incipient bearing fault features. • Selection of MODWPT as the linear transformation for APMESR. • Evaluation and comparison of period estimation methodologies and transformations. • Verification of APMESR's effectiveness through several simulations and experiments. Bearing incipient fault feature extraction is crucial and challenging throughout its life cycle. In this paper, an adaptive period matching enhanced sparse representation (APMESR) algorithm is developed to address this issue. First, a novel methodology for estimating the period of faulty impulses is proposed from the perspective of mining the periodicity-related numerical patterns. Second, the period estimation methodology is embedded in a sparse representation model to implement adaptive period matching to form APMESR, which is capable of achieving periodic sparsity. Third, maximal overlap discrete wavelet packet transform is selected as the linear transformation of APMESR for improving its ability to reduce noise and highlight periodic impulse signatures. Furthermore, evaluations and comparisons are conducted using simulations to demonstrate the validity and performance of the proposed period estimation methodology, linear transformation, and APMESR. Experimental results indicate that APMESR can effectively extract incipient bearing fault features and outperforms other well-advanced methods. [ABSTRACT FROM AUTHOR]

• A novel adaptive signal decomposition method named RVME is proposed. • Simulated and experimental cases validate the effectiveness of the proposed method. • RVME has higher computational efficiency than VMD. The variational mode extraction (VME) developed on the similar basis of variational mode decomposition (VMD) can effectively separate a specific mode by knowing an approximate center frequency from the multi-component signal. Compared with VMD, VME has made certain progress in improving the extraction accuracy and reducing the computational cost when the aim is to separate a specific mode. As the fault feature signal of rolling bearing is usually a band-limited signal which is compact around the excited resonant frequency, VME becomes a potentially effective tool for extracting fault characteristics of rolling bearing. However, how to adaptively determine the center frequency and the penalty factor are two difficult problems when using VME to separate the desired component. Accordingly, this paper presents the recursive variational mode extraction (RVME), an iterative VME-based signal decomposition algorithm. At each iteration of RVME, the initial center frequency and penalty factor for the reconstruction of a specific sub-component can be adaptively determined according to the dominant frequency of the residual signal of the previous iterative decomposition, which makes RVME an adaptive signal decomposition algorithm. The presented method is applied to simulated and experimental fault signals and compared with other classical fault feature extraction approaches, such as variational mode decomposition (VMD) and spectral kurtosis (SK). The results confirm that the established method can extract the fault features as effective as VMD, but it is significantly better than VMD in terms of computational efficiency. Meanwhile, the proposed method shows a stronger capability of weak bearing fault feature extraction and compound fault feature separation compared with SK. [ABSTRACT FROM AUTHOR]

Enhancers are regulatory elements involved in gene expression.It is a part of DNA, which can enhance the transcription rate of gene. However, the identification of enhancer by biological experimental methods is time-consuming and expensive. Therefore, there is an urgent need for more efficient methods to identify them.In this study, we propose a new feature extraction method RKPK, which combines three feature methods and uses the recursive feature elimination algorithm for feature selection, and apply deep neural network as classifier to construct the iEnhancer-RD calculation method for enhancer identification. It is a two-layer classification architecture in which the first layer(layer I) identifies enhancers from a set of DNA sequences, and the second layer(layer II) divides the identified enhancers into two subgroups, namely strong and weak enhancers. Independent dataset test indicates that the proposed method is significantly better than most existing methods, and attains the accuracy of 78.8% and 70.5% in the two layers, respectively. Our iEnhancer-RD architecture is implemented in Python and is available at https://github.com/YangHuan639/iEnhancer-RD. [Display omitted] • In this paper, we propose iEnhancer-RD, a method to identify enhancers and their strength. • We propose new feature methods KPCV and RKPK, and compare and analyze these two representations. • Deep neural network (DNN) is used as our classififier. • Optimize several feature methods and the parameters of the constructed deep neural network. • Compared with other work, this method has signifificant improvement in most performance indicators. [ABSTRACT FROM AUTHOR]

• A sign language recognition framework based on global-local attention, including: • A Multiple Group Random Segment Sampling algorithm to extract the key frame clips; • A global Attention-Model based ResC3D network with an improved timing conversion layer; • A local SLR algorithm based on Mask RCNN hand detection network. Video-level sign language recognition is still a challenging task due to the influence of sign language-independent factors and timing requirements. This paper constructs a sign language recognition framework based on global-local feature description, and proposes a three-dimensional residual global network model with attention layer and a local network model based on target detection. The global feature description is based on the whole video behavior for time series modeling. The improved timing conversion layer is used to explore the timing information of different periods and learn the video representations of different timings. In the local module the hand is located through the target detection network to highlight its key role in the whole sign language behavior, which strengthens the category differences, and compensates the global network. Experiments on two well-known Chinese sign language datasets (SLR_Dataset and DEVSIGN_D) show that the proposed method can obtain higher recognition accuracy (respectively 89.2%, 91%) and better generalization performance. [ABSTRACT FROM AUTHOR]

• We develop a detection algorithm and analyze results for different occluded sizes. • We selectively drop objects' features to enrich the occluded scenes. • We learn the implicit mapping to recover occlude noise to real feature. • We verify the effect of GC-FRCN and apply it to the in-filed Rice Panicles Counting. Detecting the object with external occlusion has always been a hot topic in computer version, while its accuracy is always limited due to the loss of original object information and increase of new occlusion noise. In this paper, we propose a occluded object detection algorithm named GC-FRCN (Generative feature completing Faster RCNN), which consists of the OSGM (Occlusion Sample Generation Module) and OSIM (Occlusion Sample Inpainting Module). Specifically, the OSGM mines and discards the feature points with high category response on the feature map to enhance the richness of occlusion scenes in the training data set. OSIM learns an implicit mapping relationship from occluded feature map to real feature map adversarially, which aims at improving feature quality by repair the noisy object feature. Extensive experiments and ablation studies have been conducted on four different datasets. All the experiments demonstrate the GC-FRCN can effectively detect objects with local external occlusion and has good robustness for occlusion at different scales. [ABSTRACT FROM AUTHOR]