Infrared small target detection is crucial in military reconnaissance, remote sensing, and so on. However, due to its small size and the high coupling with complex backgrounds, the present methods still face challenges in precise detection. They predominantly focus on target feature learning while neglecting the critical role of background modeling for small target decoupling. To this end, we propose a dual-task collaborative optimization network (DCONet), which decouples the task into background estimation and target segmentation using a multistage iterative optimization strategy. First, considering significant directional distribution characteristics in infrared backgrounds, we propose a direction-aware background estimation module (DBEM) to capture directional features, such as clouds and trees, thereby generating an initial background estimation. Second, we propose a background suppression gating unit (BSGU), which employs a gating mechanism and a channel-level adjustment factor to dynamically suppress background noise based on the preliminary background estimation, thereby generating the target segmentation result. Finally, the estimated background, target segmentation, and the reconstructed original image based on them are propagated to the next stage for further iterative optimization. The experimental results show that DCONet performs better than existing methods across three public datasets. The source code is available athttps://github.com/tustAilab/DCONet

In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered "black boxes," making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0.56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.

Summary The development of lightweight networks makes neural networks more efficient to be widely applied to various tasks. Considering the deployment of hardware like edge devices and mobile phones, we prioritize lightweight networks. However, their accuracy has always lagged far behind SOTA networks. In this article, we present a simple yet effective activation function, called WReLU, to improve the performance of lightweight networks significantly by adding a residual spatial condition. Moreover, we use a strategy to switch activation functions after determining which convolutional layer to use. We perform experiments on ImageNet 2012 classification dataset in CPU, GPU, and edge devices. Experiments demonstrate that WReLU improves the accuracy of classification significantly. Meanwhile, our strategy balances the effect of additional parameters and multiply accumulate. Our method improves the accuracy of SqueezeNet and SqueezeNext by more than 5% without increasing extensive parameters and computation. For the lightweight network with a large number of parameters, such as MobileNet and ShuffleNet, there is also a significant improvement. Additionally, the inference speed of most lightweight networks using our WReLU strategy is almost the same as the baseline model on different platforms. Our approach not only ensures the practicability of the lightweight network but also improves its performance.

Accurate classification and comprehensive explanation is crucial to build a computer aided diagnosis (CAD) system of diffuse lung disease (DLD). Although deep neural networks (DNNs) have been applied to this task, the classification performance and reliability are not satisfied for medical clinical requirements. Specifically, DNNs are regarded as unexplainable “black-box” in general, and, thus, are not deemed reliable by expects. In this paper, we propose a neural network structure search approach based on evolutionary algorithm to improve the DNN's effectiveness and interpretability, and applied to the pulmonary textures classification problem. Through this network structure search approach, we find out how a DNN's subnet recognize the pulmonary textures features, then filter out the redundant subnets, and retain the most distinctive feature subnets. Besides, we utilize the method of feature visualization and the fine-grained heat map of the activation to interpret network's decision-making process. Finally, through quantitatively and qualitatively evaluate on a real dataset of diffuse lung disease, we verify the effectiveness of this neural network structure search approach on VggNet and ResNet, and achieve the state-of-the-art performance. We can classify the pulmonary textures on high-resolution computed tomography (HRCT) images.

For a non‐linear sucker‐rod pumping system, a surface dynamometer card estimation algorithm based on a particle filter is presented. The dynamometer card is a plot of the polished rod load at various positions of a pump stroke. Since the polished rod load measured by a load sensor is frequently affected by drift problems, a local characteristic correlation method is proposed while building the state‐space model for the pumping unit. The local characteristic correlation method makes the system insensitive to load drift problems. Moreover, the prior data recorded from different wells are used to construct the importance density. To make the k ‐time importance density closer to the real posterior distribution, current measurement information is used. The performance of the proposed algorithm is evaluated on the actual operating data of a Xinjiang oil field containing typical daily production activities that can cause sudden system state changes. The results show that the proposed algorithm can adapt to sudden changes of the underground environment caused by various human factors, and it can provide robust estimation for multi‐well long‐term state tracking.