Xiaolin Xing¹, Shujie Yang^1,*, Bohan Li²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1585-1597, 2023, DOI:10.32604/cmc.2023.035415

Abstract Image Super-Resolution (SR) research has achieved great success with powerful neural networks. The deeper networks with more parameters improve the restoration quality but add the computation complexity, which means more inference time would be cost, hindering image SR from practical usage. Noting the spatial distribution of the objects or things in images, a two-stage local objects SR system is proposed, which consists of two modules, the object detection module and the SR module. Firstly, You Only Look Once (YOLO), which is efficient in generic object detection tasks, is selected to detect the input images for obtaining objects of interest, then… More >

Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Wilbur L. Walters, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.5, pp. 15-30, 2023, DOI:10.32604/jai.2023.041341

Abstract The object detection technique depends on various methods for duplicating the dataset without adding more images. Data augmentation is a popular method that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization. This method is recommended in the case where the amount of high-quality data is limited, and gaining new examples is costly and time-consuming. In this paper, we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes (Car, Bus, Motorcycle, and Person). We used five different data augmentations… More >

Sumin Hong¹, Jin-Woo Jeong^2,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1611-1632, 2023, DOI:10.32604/csse.2023.038319

Abstract As the demand for used books has grown in recent years, various online/offline market platforms have emerged to support the trade in used books. The price of used books can depend on various factors, such as the state of preservation (i.e., condition), the value of possession, and so on. Therefore, some online platforms provide a reference document to evaluate the condition of used books, but it is still not trivial for individual sellers to determine the price. The lack of a standard quantitative method to assess the condition of the used book would confuse both sellers and consumers, thereby decreasing… More >

Xujun Li^*, Guodong Fang, Liming Rao, Tengze Zhang

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2671-2688, 2023, DOI:10.32604/csse.2023.038154

Abstract To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware… More >

Chen Su, Jie Hong, Jiang Wang, Yang Yang^*

Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2611-2632, 2023, DOI:10.32604/phyton.2023.029457

Abstract The statistics of the number of rapeseed seedlings are very important for breeders and planters to conduct seed quality testing, field crop management and yield estimation. Calculating the number of seedlings is inefficient and cumbersome in the traditional method. In this study, a method was proposed for efficient detection and calculation of rapeseed seedling number based on improved you only look once version 5 (YOLOv5) to identify objects and deep-sort to perform object tracking for rapeseed seedling video. Coordinated attention (CA) mechanism was added to the trunk of the improved YOLOv5s, which made the model more effective in identifying shaded,… More >

Huabin Wang¹, Ruichao Mo², Yuping Chen³, Weiwei Lin^2,4,*, Minxian Xu⁵, Bo Liu^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2025-2047, 2023, DOI:10.32604/cmes.2023.026910

Abstract Nowadays, the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network, such as pedestrian and vehicle detection, to provide efficient intelligent services to mobile users. However, as the accuracy requirements continue to increase, the components of deep learning models for pedestrian and vehicle detection, such as YOLOv4, become more sophisticated and the computing resources required for model training are increasing dramatically, which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance. For addressing this challenge, a cloud-edge… More >

Qiang Luo¹, Wenbin Zheng^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2227-2246, 2023, DOI:10.32604/iasc.2023.040195

Abstract In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original image, and the refined classification… More >

Wei Wu¹, Xiulai Li², Zhuhua Hu¹, Xiaozhang Liu^3,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 489-498, 2023, DOI:10.32604/cmc.2023.039929

Abstract In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed anchor boxes utilized in conventional YOLOv7 models with a set of more suitable anchor boxes specifically designed based on the size distribution of ships in the dataset. This paper also introduces a novel multi-scale feature fusion module, which comprises Path Aggregation Network (PAN) modules, enabling the efficient capture of ship features across different scales. Furthermore, data preprocessing is enhanced through the application of data… More >

Zunliang Chen^1,2, Chengxu Huang^1,2, Lucheng Duan^1,2, Baohua Tan^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451

Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >

Shuja Ali¹, Ahmad Jalal¹, Mohammed Hamad Alatiyyah², Khaled Alnowaiser³, Jeongmin Park^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114

Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal of this study is to… More >