Conghao Niu^1,*, Dezhi Han¹, Bing Han², Zhongdai Wu²

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1723-1748, 2024, DOI:10.32604/csse.2024.056736 - 22 November 2024

Abstract The high coverage and all-weather capabilities of Synthetic Aperture Radar (SAR) image ship detection make it a widely accepted method for maritime ship positioning and identification. However, SAR ship detection faces challenges such as indistinct ship contours, low resolution, multi-scale features, noise, and complex background interference. This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images, incorporating key structures to enhance performance. The YOLOv8 backbone is replaced by the Slim Backbone (SB), and the Delete Medium-sized Detection Head (DMDH) structure is eliminated to concentrate on shallow features. Dynamically adjusting the… More >

Hina Bhanbhro^1,*, Yew Kwang Hooi¹, Mohammad Nordin Bin Zakaria¹, Worapan Kusakunniran², Zaira Hassan Amur¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2243-2259, 2024, DOI:10.32604/cmc.2024.052138 - 18 November 2024

Abstract Object detection has made a significant leap forward in recent years. However, the detection of small objects continues to be a great difficulty for various reasons, such as they have a very small size and they are susceptible to missed detection due to background noise. Additionally, small object information is affected due to the downsampling operations. Deep learning-based detection methods have been utilized to address the challenge posed by small objects. In this work, we propose a novel method, the Multi-Convolutional Block Attention Network (MCBAN), to increase the detection accuracy of minute objects aiming to… More >

Sheng Xiang¹, Junhao Ma¹, Qunli Shang¹, Xianbao Wang^1,*, Defu Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 713-731, 2024, DOI:10.32604/cmes.2024.052759 - 20 August 2024

Abstract Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection. However, small objects are difficult to detect accurately because they contain less information. Many current methods, particularly those based on Feature Pyramid Network (FPN), address this challenge by leveraging multi-scale feature fusion. However, existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers, leading to suboptimal small object detection. To address this problem, we propose the Two-layer Attention Feature Pyramid Network (TA-FPN), featuring two key modules: the Two-layer Attention Module (TAM) and the… More > Graphic Abstract

Meng Huang, Honglei Wei^*, Xianyi Zhai

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510 - 25 April 2024

Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution… More >

Xiangyan Tang^1,2, Chengchun Ruan^1,2,*, Xiulai Li^2,3, Binbin Li^1,2, Cebin Fu^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 983-1003, 2024, DOI:10.32604/cmc.2024.047541 - 25 April 2024

Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to… More >

Xiao Lu^1,*, Chengling Jiang¹, Zhoujun Ma¹, Haitao Li², Yuexin Liu²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 373-390, 2024, DOI:10.32604/cmc.2024.047469 - 25 April 2024

Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect… More >

Changfeng Feng¹, Chunping Wang², Dongdong Zhang¹, Renke Kou¹, Qiang Fu^1,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3993-4013, 2024, DOI:10.32604/cmc.2024.048351 - 26 March 2024

Abstract Transformer-based models have facilitated significant advances in object detection. However, their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle (UAV) imagery. Addressing these limitations, we propose a hybrid transformer-based detector, H-DETR, and enhance it for dense small objects, leading to an accurate and efficient model. Firstly, we introduce a hybrid transformer encoder, which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently. Furthermore, we propose two novel strategies to enhance detection performance without incurring additional inference… More >

Weiya Shi^1,*, Shaowen Zhang², Shiqiang Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3209-3231, 2024, DOI:10.32604/cmes.2023.044863 - 11 March 2024

Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy.… More >

Xinyu Hu, Defeng Kong^*, Xiyang Liu, Junwei Zhang, Daode Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376 - 30 January 2024

Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention… More >

Jian Wei, Qinzhao Wang^*, Zixu Zhao

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1699-1717, 2023, DOI:10.32604/cmc.2023.044284 - 29 November 2023

Abstract The detection of large-scale objects has achieved high accuracy, but due to the low peak signal to noise ratio (PSNR), fewer distinguishing features, and ease of being occluded by the surroundings, the detection of small objects, however, does not enjoy similar success. Endeavor to solve the problem, this paper proposes an attention mechanism based on cross-Key values. Based on the traditional transformer, this paper first improves the feature processing with the convolution module, effectively maintaining the local semantic context in the middle layer, and significantly reducing the number of parameters of the model. Then, to More >