Lightweight Deep Convolutional Network Research Articles

A lightweight deep convolutional network with inverted residuals for matching optical and SAR images

ABSTRACT Matching optical and synthetic aperture radar (SAR) images is often challenged by intricate geometric distortion and nonlinear radiation differences, leading to insufficient and unevenly distributed corresponding points. To tackle this issue, we propose a lightweight deep convolutional network with inverted residuals for optical and SAR image matching. Initially, a fully convolutional neural network (FCNN) is designed to extract high-level and semantic features, robustly capturing universal characteristics between optical and SAR images, adept at handling geometric distortion and nonlinear radiation changes. Notably, we integrate a lightweight architecture with inverted residuals into FCNN to adeptly extract local and global contextual information, facilitating feature reuse and minimizing the loss of crucial features. Additionally, a vector-refined module is deployed to refine dense features, filtering out redundant information. Subsequently, a coarse-to-fine strategy is employed to further eliminate gross errors or incorrect matches. Finally, we evaluate the performance of the proposed network in optical and SAR image matching against manually-designed methods and state-of-the-art deep learning techniques. Experimental results demonstrate that our network significantly surpasses existing methods in terms of the number of correct matches and matching accuracy. Specifically, our proposed network achieves at least a 2.8 times increase in correct matches and an 18% improvement in matching accuracy compared to existing methods.

Autonomous Identification of Bridge Concrete Cracks Using Unmanned Aircraft Images and Improved Lightweight Deep Convolutional Networks

The identification of the development of structural defects is an important part of bridge structure damage diagnosis, and cracks are considered the most typical and highly dangerous structural disease. However, existing deep learning-based methods are mostly aimed at the scene of concrete cracks, while they rarely focus on designing network architectures to improve the vision-based model performance from the perspective of unmanned aircraft system (UAS) inspection, which leads to a lack of specificity. Because of this, this study proposes a novel lightweight deep convolutional neural network-based crack pixel-level segmentation network for UAS-based inspection scenes. Firstly, the classical encoder-decoder architecture UNET is utilized as the base model for bridge structural crack identification, and the hourglass-shaped depthwise separable convolution is introduced to replace the traditional convolutional operation in the UNET model to reduce model parameters. Then, a kind of lightweight and efficient channel attention module is used to improve model feature fuzzy ability and segmentation accuracy. We conducted a series of experiments on bridge structural crack detection tasks by utilizing a long-span bridge as the research item. The experimental results show that the constructed method achieves an effective balance between reasoning accuracy and efficiency with the value of 97.62% precision, 97.23% recall, 97.42% accuracy, and 93.25% IOU on the bridge concrete crack datasets, which are significantly higher than those of other state-of-the-art baseline methods. It can be inferred that the application of hourglass-shaped depth-separable volumes can actively reduce basic model parameters. Moreover, the lightweight and efficient attention modules can achieve local cross-channel interaction without dimensionality reduction and improve the network segmentation performance.

Refinement Bilateral Segmentation Network for Semantic Segmentation in Traffic Scenes

Semantic segmentation technology in traffic scenes can help vehicles make accurate analyses and positioning of the road ahead. In traffic scenarios, the trade-off between real-time performance and the accuracy of semantic segmentation is particularly important. This paper proposes a lightweight deep convolutional network, which can be applied to traffic scenes to complete accurate semantic segmentation tasks, considering both real-time performance and accuracy. The Cross Channel Attention Fusion Mechanism proposed in this paper can better integrate the context information and improve accuracy. The Depth-wise Separable Pyramid Module proposed based on the feature pyramid idea can improve the segmentation accuracy and effectively trade off the real-time performance.

An Anchor-Free Lightweight Deep Convolutional Network for Vehicle Detection in Aerial Images

Vehicle object detection in aerial scenes has important applications in both military and civilian fields. Recently, deep learning has shown clear advantages in object detection, and the detection performance has been continuously improved. However, these deep object detection algorithms rely on anchor-based approaches accompanied by complex convolutional operations. In this paper, we establish a lightweight aerial vehicle object detection algorithm based on the method of anchor-free. The anchor-free based object detection method effectively gets rid of the limitation of detection model capability by the size of fixed anchor box, which reduces the set of parameters and provides a more flexible solution space. In addition, the proposed lightweight object feature extraction network effectively reduces the computational cost of the model, while improving the feature extraction capability of small objects. Besides, we use channel stacking to improve the object feature extraction capability of the lightweight network, and introduce the attention mechanism in the detection model to improve the efficiency of resource utilization. We evaluate the proposed detection algorithm on both the public aerial dataset and our collected aerial dataset, and the results show that our algorithm has significant advantages over other detection algorithms in detection accuracy and efficiency. The proposed detection algorithm achieves 89.1% and 92.6% mAP on the Munich dataset and the created dataset, and the detection time for each image is 1.21s and 0.036s, respectively.

A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network

The problem of the underwater structure disease of the bridge is increasingly obvious, which has seriously affected the safe operation of the bridge structure, so it is necessary to detect the underwater structure regularly. There are many kinds of bridge underwater structure diseases. This paper targets the bridge underwater structural crack diseases adopts multiple image recognition networks for verification, compares the advantages of different networks, and takes the YOLO-v4 network as the main body to build a lightweight convolutional neural network.Mobilenetv3 replaced CSPDarkent as the backbone feature extraction network, while the feature layer scale of Mobilenetv3 was modified, and the extracted preliminary feature layer was input into the enhanced feature extraction network for feature fusion. The PANet networks are replaced by the depthwise separable convolution. Using ablation experiments to compare the performance of four algorithm combinations in lightweight networks. At the same time, the disease identification accuracy of each network and the performance of the network are tested in various experimental environments, and the feasibility of the lightweight network is verified in the application of bridge underwater structure damage identification.

DPC-Net: Distributed Point Convolution Network for large-scale point clouds semantic segmentation

Background: Applying convolution neural networks for large-scale 3D point clouds semantic segmentation is quiet challenging, due to the unordered characteristics of 3D data and the computation burden of large-scale point clouds. Methods: To solve these problems, we designed DPC-Net (Distributed Point Convolution Network). The input point clouds of DPC-Net are partitioned by the K-nearest neighbor strategy and reordered based on Euclidean distance. For reducing computation and memory consumption while retaining critical features, the random sampling strategy is used and a distributed point convolution operation is designed. Our novel convolution method extracts parallel local geometric information including space distance and angle features, respectively. Furthermore, our proposed method could be easily and efficiently embedded into many networks for point clouds semantic segmentation. Results: Extensive experimental results on the Semantic3D and CSPC (Complex Scene Point Cloud) datasets indicate that the proposed DPC-Net not only obtains state-of-the-art performances but also reduces semantic segmentation time. Conclusions: In general, we present an efficient and lightweight deep convolutional network, DPC-Net, which captures local geometric features and local contextual information to predict point labels.

Vehicle detection in aerial images based on lightweight deep convolutional network

Vehicle detection in aerial images based on lightweight deep convolutional network

Vehicle Detection in Aerial Images Based on Lightweight Deep Convolutional Network and Generative Adversarial Network

Vehicle detection in aerial images is a challenging task and plays an important role in a wide range of applications. Traditional detection algorithms are based on sliding-window searching and shallowlearning-based features, which limits the ability to represent features and generates a lot of computational costs. Recently, with the successful application of convolutional neural network in computer vision, many state-of-the-art detectors have been developed based on deep CNNs. However, these CNN-based models still face some difficulties and challenges in vehicle detection in aerial images. Firstly, the CNN-based detection model requires extensive calculations during training and detection, and the accuracy of detection for small objects is not high. In addition, deep learning models often require a large amount of sample data to train a robust detection model, while the annotated data of aerial vehicles is limited. In this study, we propose a lightweight deep convolutional neural network detection model named LD-CNNs. The detection algorithm not only greatly reduces the computational costs of the model, but also significantly improves the accuracy of the detection. What’s more, in order to cope with the problem of insufficient training samples, we develop a multi-condition constrained generative adversarial network named MC-GAN, which can effectively generate samples. The detection performance of the proposed model has been evaluated on the Munich public dataset and the collected dataset respectively. The results show that on the Munich dataset, the proposed method achieves 86.9% on mAP (mean average precision), F1-score is 0.875, and the detection time is 1.64s on Nvidia Titan XP. At present, these detection indicators have reached state-of-the-art level in vehicle detection of aerial images.