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Syst."},{"key":"ref25","first-page":"1","article-title":"Hybrid knowledge R-CNN for transmission line multifitting detection","volume":"70","author":"Zhai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref26","series-title":"2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","first-page":"27","article-title":"Deep residual learning for image recognition","author":"He","year":"Jun. 2016"},{"key":"ref27","series-title":"2017 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","first-page":"21","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"Jul. 2017"},{"key":"ref28","series-title":"2018 IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"18\u201323 Jun. 2018"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"B. 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