Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection

The detection of vibration dampers is important for power systems. Deep learning-based damper detection needs massive annotations, which are labor-intensive and time-consuming. Therefore, weakly supervised object detection (WSOD) is considered. Part domination is the main problem in weakly supervised vibration damper detection. Current WSOD methods neglect that overwhelming negative instances exist in each image during the training phase, which would mislead the training and make detection results stuck in the most discriminative parts of objects. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this article. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module, combining random sampling and intersection over union (IoU)-balanced sampling, progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances to make the network focus on positive instances. Extensive experimental results on the vibration damper, pattern analysis, statistical modelling and computational learning visual object classes (PASCAL VOC) 2007, and PASCAL VOC 2012 datasets demonstrate that the proposed method can significantly improve the baseline, which is also comparable to many existing methods. In addition, compared with the baseline, the proposed method requires no extra network parameters, and the supplementary training overheads are small.