@InProceedings{AyalaMacZanCruFer:2021:EfMuFi,
               author = "Ayala, Angel and Mac{\^e}do, David and Zanchettin, Cleber and 
                         Cruz, Francisco and Fernandes, Bruno",
          affiliation = "Escola Polit{\'e}cnica de Pernambuco, Universidade de Pernambuco 
                         and Centro de Inform{\'a}tica, Universidade Federal de Pernambuco 
                         and Centro de Inform{\'a}tica, Universidade Federal de Pernambuco 
                         and School of Information Technology, Deakin University and Escola 
                         Polit{\'e}cnica de Pernambuco, Universidade de Pernambuco",
                title = "KutralNext: An Efficient Multi-label Fire and Smoke Image 
                         Recognition Model",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "efficient approach, fire recogntion, smoke recogntion, deep 
                         learning.",
             abstract = "Early alert fire and smoke detection systems are crucial for 
                         management decision making as daily and security operations. One 
                         of the new approaches to the problem is the use of images to 
                         perform the detection. Fire and smoke recognition from visual 
                         scenes is a demanding task due to the high variance of color and 
                         texture. In recent years, several fire-recognition approaches 
                         based on deep learning methods have been proposed to overcome this 
                         problem. Nevertheless, many developments have been focused on 
                         surpassing previous state-of-the-art model's accuracy, regardless 
                         of the computational resources needed to execute the model. In 
                         this work, is studied the trade-off between accuracy and 
                         complexity of the inverted residual block and the octave 
                         convolution techniques, which reduces the model's size and 
                         computation requirements. The literature suggests that those 
                         techniques work well by themselves, and in this research was 
                         demonstrated that combined, it achieves a better trade-off. We 
                         proposed the KutralNext architecture, an efficient model with 
                         reduced number of layers and computacional resources for single- 
                         and multi-label fire and smoke recognition tasks. Additionally, a 
                         more efficient KutralNext+ model improved with novel techniques, 
                         achieved an 84.36% average test accuracy in FireNet, FiSmo, and 
                         FiSmoA fire datasets. For the KutralSmoke and FiSmo fire and smoke 
                         datasets attained an 81.53\% average test accuracy. Furthermore, 
                         state-of-the-art fire and smoke recognition model considered, 
                         FireDetection, KutralNext uses 59% fewer parameters, and 
                         KutralNext+ requires 97% fewer flops and is 4x faster.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CTDF8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CTDF8",
           targetfile = "kutralnext_CameraReady.pdf",
        urlaccessdate = "2024, May 02"
}