Rare-class extraction using cascaded pretrained networks applied to crane classification

Overweight vehicles are a common source of pavement and bridge damage. Especially mobile crane vehicles are often beyond legal per-axle weight limits, carrying their lifting blocks and ballast on the vehicle instead of on a separate trailer. To prevent road deterioration, the detection of overweight cranes is desirable for law enforcement. As the source of crane weight is visible, we propose a camera-based detection system based on convolutional neural networks. We iteratively label our dataset to vastly reduce labeling and extensively investigate the impact of image resolution, network depth and dataset size to choose optimal parameters during iterative labeling. We show that iterative labeling with intelligently chosen image resolutions and network depths can vastly improve (up to 70×) the speed at which data can be labeled, to train classification systems for practical surveillance applications. The experiments provide an estimate of the optimal amount of data required to train an effective classification system, which is valuable for classification problems in general. The proposed system achieves an AUC score of 0.985 for distinguishing cranes from other vehicles and an AUC of 0.92 and 0.77 on lifting block and ballast classification, respectively. The proposed classification system enables effective road monitoring for semi-automatic law enforcement and is attractive for rare-class extraction in general surveillance classification problems.