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  • Open Access


    MPFracNet: A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties

    Geng Qin1, Ping Luo1, Kaiyuan Li1, Yufeng Sun1, Shiwei Wang1, Xiaoting Li1,2,3, Shuang Liu1,2,3, Linyan Xue1,2,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 999-1015, 2023, DOI:10.32604/cmc.2023.035777

    Abstract Due to small size and high occult, metacarpophalangeal fracture diagnosis displays a low accuracy in terms of fracture detection and location in X-ray images. To efficiently detect metacarpophalangeal fractures on X-ray images as the second opinion for radiologists, we proposed a novel one-stage neural network named MPFracNet based on RetinaNet. In MPFracNet, a deformable bottleneck block (DBB) was integrated into the bottleneck to better adapt to the geometric variation of the fractures. Furthermore, an integrated feature fusion module (IFFM) was employed to obtain more in-depth semantic and shallow detail features. Specifically, Focal Loss and Balanced L1 Loss were introduced to… More >

  • Open Access


    DSAFF-Net: A Backbone Network Based on Mask R-CNN for Small Object Detection

    Jian Peng1,2, Yifang Zhao1,2, Dengyong Zhang1,2,*, Feng Li1,2, Arun Kumar Sangaiah3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3405-3419, 2023, DOI:10.32604/cmc.2023.027627

    Abstract Recently, object detection based on convolutional neural networks (CNNs) has developed rapidly. The backbone networks for basic feature extraction are an important component of the whole detection task. Therefore, we present a new feature extraction strategy in this paper, which name is DSAFF-Net. In this strategy, we design: 1) a sandwich attention feature fusion module (SAFF module). Its purpose is to enhance the semantic information of shallow features and resolution of deep features, which is beneficial to small object detection after feature fusion. 2) to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when… More >

  • Open Access


    Small Object Detection via Precise Region-Based Fully Convolutional Networks

    Dengyong Zhang1,2, Jiawei Hu1,2, Feng Li1,2,*, Xiangling Ding3, Arun Kumar Sangaiah4, Victor S. Sheng5

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1503-1517, 2021, DOI:10.32604/cmc.2021.017089

    Abstract In the past several years, remarkable achievements have been made in the field of object detection. Although performance is generally improving, the accuracy of small object detection remains low compared with that of large object detection. In addition, localization misalignment issues are common for small objects, as seen in GoogLeNets and residual networks (ResNets). To address this problem, we propose an improved region-based fully convolutional network (R-FCN). The presented technique improves detection accuracy and eliminates localization misalignment by replacing position-sensitive region of interest (PS-RoI) pooling with position-sensitive precise region of interest (PS-Pr-RoI) pooling, which avoids coordinate quantization and directly calculates… More >

  • Open Access


    Feature-Enhanced RefineDet: Fast Detection of Small Objects

    Lei Zhao*, Ming Zhao

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jihpp.2021.010065

    Abstract Object detection has been studied for many years. The convolutional neural network has made great progress in the accuracy and speed of object detection. However, due to the low resolution of small objects and the representation of fuzzy features, one of the challenges now is how to effectively detect small objects in images. Existing target detectors for small objects: one is to use high-resolution images as input, the other is to increase the depth of the CNN network, but these two methods will undoubtedly increase the cost of calculation and time-consuming. In this paper, based on the RefineDet network framework,… More >

  • Open Access


    A Real Time Vision-Based Smoking Detection Framework on Edge

    Ruilong Chen1, Guangfu Zeng1, Ke Wang2, Lei Luo1,*, Zhiping Cai1

    Journal on Internet of Things, Vol.2, No.2, pp. 55-64, 2020, DOI:10.32604/jiot.2020.09814

    Abstract Smoking is the main reason for fire disaster and pollution in petrol station, construction site and warehouse. Existing solutions based on wearable devices and smoking sensors were costly and hard to obtain evidence of smoking in unmanned scenarios. With the developments of closed circuit television (CCTV) system, vision-based methods for object detection, mostly driven by deep learning techniques, were introduced recently. However, the massive GPU computing hardware required by the deep learning algorithm made these methods hard to be deployed. This paper aims at solving the smoking detection problem on edge and proposes the solution that has fast detection speed,… More >

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