The segmentation and classification of leaves in plant images are a great challenge, especially when several leaves are overlapping in images with a complicated background. In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are studied. First, more than 2500 leaf images with a complicated background are collected and artificially labeled with target pixels and background pixels. Two-thousand of them are fed into a Mask Region-based Convolutional Neural Network (Mask R-CNN) to train a model for leaf segmentation. Then, a training set that contains more than 1500 training images of 15 species is fed into a very deep convolutional network with 16 layers (VGG16) to train a model for leaf classification. The best hyperparameters for these methods are found by comparing a variety of parameter combinations. The results show that the average Misclassification Error ($ME$) of 80 test images using Mask R-CNN is 1.15%. The average accuracy value for the leaf classification of 150 test images using VGG16 is up to 91.5%. This indicates that these methods can be used to segment and classify the leaf image with a complicated background effectively. It could provide a reference for the phenotype analysis and automatic classification of plants.

Rainy degeneration damages an image’s visual effect and influences the performance of subsequent vision tasks. Various deep learning methods for single image deraining have been proposed, obtaining appropriate recovery results. Unfortunately, most existing methods ignore the interaction between rain-layer and rain-free components when extracting relevant features, leading to undesirable results. To break the above limitations, we propose a progressive hybrid-modulated network (PHMNet) for single image deraining based on the two-branch and coarse-to-fine framework. Specifically, a hybrid-modulated module (HMM) with a two-branch framework is proposed to blend and modulate the feature of rain-free layers and rain streaks. After cascading several HMMs in the coarsest reconstructed stage of the PHMNet, a multi-level refined module (MLRM) is adopted to refine the final deraining results in the refined reconstructed stage. By being trained using loss functions such as contrastive learning, the PHMNet can obtain satisfactory deraining results. Extended experiments on several datasets and downstream tasks demonstrate that our method performs favorably against state-of-the-art methods in quantitative evaluation and visual effects.

A new method is proposed in (Ivković, Marković, Ivković, & Cvetanović, 2017) to measure the equivalent series resistance (ESR) and the inductance of a coil at low frequencies. However, a special phenomenon has appeared at high frequencies. It is found that the intercept has appeared and the measured inductance value had a large deviation from its nominal value. According to the analysis of result, an amended method is proposed in this paper. The inductance value should be amended according to the linear relation between the measured value and the nominal value.

Chiral active particles (CAPs) exhibit intricate collective behaviors, yet their characterization via traditional simulations or experiments is often labor-intensive and resource-demanding. To overcome these challenges, we introduce an integrated experimental platform and deep learning (DL) framework designed to predict CAP collective motion dynamics within confining structures. Our platform efficiently captures extensive spatio-temporal data of CAP assemblies under controlled vibration. The predictive model uniquely leverages particle density maps derived from experimental video-a crucial representation that effectively mitigates extraneous RGB image interference and enhances robustness against visual noise, thereby enabling the model to directly capture and learn collective states. The DL architecture is trained to generate the temporal evolution of these density maps, successfully capturing complex emergent phenomena like chiral separation. Rigorous validation demonstrates the framework’s effectiveness, achieving a low average relative error of 8.94% for the predicted minimum enclosing circle radius of particle clusters. It demonstrates strong generalizability across time (15 future frames from 5 inputs) and novel geometries (non-concentric annuli). This data-driven approach provides significant computational efficiency over traditional simulations (e.g. active Brownian particle models, which struggle with such complexities), streamlining analysis and advancing understanding of active matter systems.