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During the process of high-speed railway online maintenance, anomaly detection of targets in grease injection facilities is extremely challenging. Environmental uncertainty (noise disturbance, various illumination conditions, changeable camera perspective, etc.) limits the performance of existing unsupervised anomaly detection methods, which tend to struggle in such complex backgrounds without prior knowledge of anomalies. Therefore, we propose a novel semi-supervised anomaly detection algorithm that only learns from anomaly-free samples and annotations while can recognize both anomaly-free and anomaly targets (key areas) and detecting anomalous targets simultaneously. We first propose a key-area recognition module (KRM) to segment anomaly-free targets and predict coordinates of both anomaly-free and anomalous key areas’ center points (key points). By using the predicted coordinates, we generated a circular mask to erase features within the key areas. Based on the erased features, a local feature reconstruction and segmentation module (LFRSM) is proposed to restore the latent embeddings within the key areas and segment both anomaly-free and anomalous key areas. The final anomaly results are obtained by analyzing the original segmentation results of KRM and the reconstructed segmentation results of LFRSM. To validate the effectiveness of the proposed algorithm, we develop a dataset, called BOLT-DET, for bolt anomaly detection in complex scenes. The proposed algorithm outperforms other existing state-of-the-art (SOTA) anomaly detection algorithms (SPADE, DFR, CFLOW, Student-Teacher, PatchCore, and DRAEM) with mean intersection over union (mIoU) of 0.66, area under the receiver operating characteristic curve (AUROC) of 0.92, and F1-Measure of 0.69 on BOLT-DET. It can accurately detect anomalies and meet the requirement of practical industrial applications.

Path planning plays an important role in autonomous robot systems. Effective understanding of the surrounding environment and efficient generation of an optimal collision-free path are both critical parts for solving path-planning problems. Although conventional sampling-based algorithms, such as the rapidly exploring random tree (RRT) and its improved optimal version (RRT*), have been widely used in path-planning problems because of their ability to find a feasible path in even complex environments, they fail to find an optimal path efficiently. To solve this problem and satisfy the two aforementioned requirements, we propose a novel learning-based path-planning algorithm which consists of a novel generative model based on the conditional generative adversarial networks (CGANs) and a modified RRT* algorithm (denoted by CGAN-RRT*). Given the map information, our CGAN model can generate an efficient probability distribution of feasible paths, which can be utilized by the CGAN-RRT* algorithm to find an optimal path with a nonuniform sampling strategy. The CGAN model is trained by learning from ground-truth maps, each of which is generated by putting all the results of executing the RRT algorithm 50 times on one raw map. We demonstrate the efficient performance of this CGAN model by testing it on two groups of maps and comparing the CGAN-RRT* algorithm with the Informed-RRT* algorithm and conventional RRT* algorithm.