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  • Open Access


    Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

    Ning Li*, Shucai Huang, Daozhi Wei

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3511-3531, 2023, DOI:10.32604/cmc.2023.045987

    Abstract This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared image. Besides, an improved… More >

  • Open Access


    DM Code Key Point Detection Algorithm Based on CenterNet

    Wei Wang1, Xinyao Tang2,*, Kai Zhou1, Chunhui Zhao1, Changfa Liu3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1911-1928, 2023, DOI:10.32604/cmc.2023.043233

    Abstract Data Matrix (DM) codes have been widely used in industrial production. The reading of DM code usually includes positioning and decoding. Accurate positioning is a prerequisite for successful decoding. Traditional image processing methods have poor adaptability to pollution and complex backgrounds. Although deep learning-based methods can automatically extract features, the bounding boxes cannot entirely fit the contour of the code. Further image processing methods are required for precise positioning, which will reduce efficiency. Because of the above problems, a CenterNet-based DM code key point detection network is proposed, which can directly obtain the four key points of the DM code.… More >

  • Open Access


    Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

    Jigna Patel1, Anand Ruparelia1, Sudeep Tanwar1,*, Fayez Alqahtani2, Amr Tolba3, Ravi Sharma4, Maria Simona Raboaca5,6,*, Bogdan Constantin Neagu7

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1281-1301, 2023, DOI:10.32604/cmc.2023.038796

    Abstract The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images.… More >

  • Open Access


    Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

    Ayesha1, Muhammad Javed Iqbal1, Iftikhar Ahmad2,*, Madini O. Alassafi2, Ahmed S. Alfakeeh2, Ahmed Alhomoud3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3647-3661, 2023, DOI:10.32604/cmc.2023.020916

    Abstract Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents. The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects. This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area. In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet. Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in… More >

  • Open Access


    An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

    Pingping Yu1, Hongda Wang1, Xiaodong Zhao1,*, Guangchen Ruan2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4261-4276, 2022, DOI:10.32604/cmc.2022.029239

    Abstract Aiming at the problems of low target image resolution, insufficient target feature extraction, low detection accuracy and poor real time in remote engineering vehicle detection, an improved CenterNet target detection model is proposed in this paper. Firstly, EfficientNet-B0 with Efficient Channel Attention (ECA) module is used as the basic network, which increases the quality and speed of feature extraction and reduces the number of model parameters. Then, the proposed Adaptive Fusion Bidirectional Feature Pyramid Network (AF-BiFPN) module is applied to fuse the features of different feature layers. Furthermore, the feature information of engineering vehicle targets is added by making full… More >

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