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We present a novel multi-tasking Faster-RCNN based approach using the Global Average Pooling(GAP) and Region of Interest (RoI) Align techniques to detect the road cracks. The RoI Align is used to avoid quantizing the stride. So that the information loss can be minimized and the bi-linear interpolation can be used to map the proposal to the input image. The resulting features from RoI Align are given as input to the GAP layer which drastically reduces the multi-dimension features into a single feature map. The output of the GAP layer is given to the fully connected layer for classification (softmax) and also to a regression model for predicting the crack location using a bounding box. F1-measure, precision, and recall were used to evaluate the results of classification and detection. The proposed model achieves the accuracy-97.97%, precision-99.12%, and recall-97.25% for classification using the MIT-CHN-ORR dataset. The experimental results show, that the proposed approach outperforms the other state-of-the-art methods.<\/jats:p>","DOI":"10.3233\/jifs-210475","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T12:53:48Z","timestamp":1626785628000},"page":"6615-6628","source":"Crossref","is-referenced-by-count":30,"title":["Automatic road crack detection and classification using multi-tasking faster RCNN"],"prefix":"10.1177","volume":"41","author":[{"given":"Aravindkumar","family":"Sekar","sequence":"first","affiliation":[{"name":"Department of Computer Technology, Anna University, MIT-Campus, Chrompet, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varalakshmi","family":"Perumal","sequence":"additional","affiliation":[{"name":"Department of Computer Technology, Anna University, MIT-Campus, Chrompet, Chennai, 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