In the process of image shooting, due to the influence of angle, distance, complex scenes, illumination intensity, and other factors, small targets and occluded targets will inevitably appear in the image. These targets have few effective pixels, few features, and no obvious features, which makes it difficult to extract their effective features and easily leads to false detection, missed detection, and repeated detection, thus affecting the performance of target detection models. To solve this problem, an improved faster region convolutional neural network (RCNN) algorithm integrating the convolutional block attention module (CBAM) and feature pyramid network (FPN) (CF-RCNN) is proposed to improve the detection and recognition accuracy of small-sized, occluded, or truncated objects in complex scenes. Firstly, it incorporates the CBAM attention mechanism in the feature extraction network in combination with the information filtered by spatial and channel attention modules, focusing on local efficient information of the feature image, which improves the detection ability in the face of obscured or truncated objects. Secondly, it introduces the FPN feature pyramid structure, and links high-level and bottom-level feature data to obtain high-resolution and strong semantic data to enhance the detection effect for small-sized objects. Finally, it optimizes non-maximum suppression (NMS) to compensate for the shortcomings of conventional NMS that mistakenly eliminates overlapping detection frames. The experimental results show that the mean average precision (MAP) of target detection of the improved algorithm on PASCAL VOC2012 public datasets is improved to 76.2%, which is 13.9 percentage points higher than those of the commonly used Faster RCNN and other algorithms. It is better than the commonly used small-sample target detection algorithm.
{"title":"Faster RCNN Target Detection Algorithm Integrating CBAM and FPN","authors":"Wenshun Sheng, Xiongfeng Yu, Jiayan Lin, Xin Chen","doi":"10.3390/app13126913","DOIUrl":"https://doi.org/10.3390/app13126913","url":null,"abstract":"In the process of image shooting, due to the influence of angle, distance, complex scenes, illumination intensity, and other factors, small targets and occluded targets will inevitably appear in the image. These targets have few effective pixels, few features, and no obvious features, which makes it difficult to extract their effective features and easily leads to false detection, missed detection, and repeated detection, thus affecting the performance of target detection models. To solve this problem, an improved faster region convolutional neural network (RCNN) algorithm integrating the convolutional block attention module (CBAM) and feature pyramid network (FPN) (CF-RCNN) is proposed to improve the detection and recognition accuracy of small-sized, occluded, or truncated objects in complex scenes. Firstly, it incorporates the CBAM attention mechanism in the feature extraction network in combination with the information filtered by spatial and channel attention modules, focusing on local efficient information of the feature image, which improves the detection ability in the face of obscured or truncated objects. Secondly, it introduces the FPN feature pyramid structure, and links high-level and bottom-level feature data to obtain high-resolution and strong semantic data to enhance the detection effect for small-sized objects. Finally, it optimizes non-maximum suppression (NMS) to compensate for the shortcomings of conventional NMS that mistakenly eliminates overlapping detection frames. The experimental results show that the mean average precision (MAP) of target detection of the improved algorithm on PASCAL VOC2012 public datasets is improved to 76.2%, which is 13.9 percentage points higher than those of the commonly used Faster RCNN and other algorithms. It is better than the commonly used small-sample target detection algorithm.","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"68 1","pages":"1549-1569"},"PeriodicalIF":2.2,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89604886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.32604/csse.2023.034172
Yudong Zhang, Muhammad Attique Khan, Ziquan Zhu, Shuihua Wang
(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.
{"title":"SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition.","authors":"Yudong Zhang, Muhammad Attique Khan, Ziquan Zhu, Shuihua Wang","doi":"10.32604/csse.2023.034172","DOIUrl":"https://doi.org/10.32604/csse.2023.034172","url":null,"abstract":"<p><p>(Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.</p>","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"46 1","pages":"13-26"},"PeriodicalIF":2.2,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9784682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.032737
K. Sreelakshmy, Himanshu Gupta, Om Prakash Verma, K. Kumar, Abdelhamied A. Ateya, N. Soliman
{"title":"3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA","authors":"K. Sreelakshmy, Himanshu Gupta, Om Prakash Verma, K. Kumar, Abdelhamied A. Ateya, N. Soliman","doi":"10.32604/csse.2023.032737","DOIUrl":"https://doi.org/10.32604/csse.2023.032737","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"14 1","pages":"2483-2503"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73334478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.028713
M. Jamunarani, C. Vasanthanayaki
{"title":"ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression","authors":"M. Jamunarani, C. Vasanthanayaki","doi":"10.32604/csse.2023.028713","DOIUrl":"https://doi.org/10.32604/csse.2023.028713","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"48 1","pages":"1953-1970"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73527005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.032977
M. Mridha, Zabir Mohammad, Muhammad Mohsin Kabir, Aklima Akter Lima, S. Das, Md. Rashedul Islam, Y. Watanobe
{"title":"An Unsupervised Writer Identification Based on Generating Clusterable燛mbeddings","authors":"M. Mridha, Zabir Mohammad, Muhammad Mohsin Kabir, Aklima Akter Lima, S. Das, Md. Rashedul Islam, Y. Watanobe","doi":"10.32604/csse.2023.032977","DOIUrl":"https://doi.org/10.32604/csse.2023.032977","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"75 1","pages":"2059-2073"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72610405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.039503
Daniyal Amankeldin, L. Kurmangaziyeva, A. Mailybayeva, Natalya Glazyrina, A. Zhumadillayeva, Nurzhamal Karasheva
,
，
{"title":"Deep Neural Network for Detecting Fake Profiles in Social Networks","authors":"Daniyal Amankeldin, L. Kurmangaziyeva, A. Mailybayeva, Natalya Glazyrina, A. Zhumadillayeva, Nurzhamal Karasheva","doi":"10.32604/csse.2023.039503","DOIUrl":"https://doi.org/10.32604/csse.2023.039503","url":null,"abstract":",","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"12 1","pages":"1091-1108"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72833485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.025503
A. Jameer Basha, S. Aswini, S. Aarthini, Yun-Seung Nam, M. Abouhawwash
{"title":"Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network","authors":"A. Jameer Basha, S. Aswini, S. Aarthini, Yun-Seung Nam, M. Abouhawwash","doi":"10.32604/csse.2023.025503","DOIUrl":"https://doi.org/10.32604/csse.2023.025503","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"1 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69723411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.032509
G. Selvakumar, L. Jayashree, S. Arumugam
{"title":"Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications","authors":"G. Selvakumar, L. Jayashree, S. Arumugam","doi":"10.32604/csse.2023.032509","DOIUrl":"https://doi.org/10.32604/csse.2023.032509","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"64 1","pages":"1215-1231"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74462928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.32604/csse.2023.032303
C. Thilaga, P. B. Sarasija
{"title":"Small-World Networks with Unitary Cayley Graphs for Various Energy Generation","authors":"C. Thilaga, P. B. Sarasija","doi":"10.32604/csse.2023.032303","DOIUrl":"https://doi.org/10.32604/csse.2023.032303","url":null,"abstract":"","PeriodicalId":50634,"journal":{"name":"Computer Systems Science and Engineering","volume":"25 1","pages":"2773-2782"},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80160766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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