Traditional manual crack inspection methods often face limitations in terms of efficiency, safety, and consistency. To overcome these issues, a new approach based on autonomous robotic systems has gained attention, combining robotics, artificial intelligence, and advanced sensing technologies. However, most existing reviews focus on individual components in isolation and fail to present a complete picture of how these systems work together. This study focuses on robotic crack detection and proposes a structured framework that connects three core modules: the physical platform (robots and sensors), the cognitive core (crack detection algorithms), and autonomous action (navigation and planning). We analyze key technologies, their interactions, and the challenges involved in real-world implementation. The aim is to provide a clear roadmap of current progress and future directions, helping researchers and engineers better understand the field and develop smart, deployable systems for infrastructure crack inspection.

Crack detection in mobile inspection scenarios is constrained by both the extremely slender geometry of crack targets and the real-time inference requirements on edge devices, which expose systematic limitations of general-purpose object detectors. This paper proposes YOLO-Crack, a closed-loop solution that couples geometry-statistics-driven module design with end-to-end edge deployment validation. On the algorithmic side, we first quantify crack geometric properties and then introduce (i) a crack-aware cross-dimensional fusion attention (CFCA) module to strengthen feature representations, (ii) a dual-path feature enhancement module (DFEM) to preserve fine details during upsampling, and (iii) an empirical smooth quality window adjustment with shape consistency regularization to stabilize bounding-box regression for slender cracks. Experiments on the Crack500 dataset show that YOLO-Crack achieves 78.8% precision, 51.4% recall, and 65.7% mAP@0.5, improving over the YOLOv11n baseline by 4.2, 1.7, and 2.9 percentage points, respectively. On the engineering side, we deploy YOLO-Crack on a Jetson Orin NX mobile robot platform and evaluate it in a real ROS pipeline; the measured end-to-end throughput reaches 25.5 FPS, meeting real-time video processing requirements. The proposed framework provides a practical reference workflow for edge vision tasks, from geometry analysis to engineering verification.

Indoor temperature forecasting in large public buildings is affected by heating, ventilation, and air-conditioning (HVAC) control, occupancy, airflow, and zone use, not only by recent temperature histories. This study asks whether operationally derived functional-zone proxy labels—room groupings derived heuristically from HVAC statistics rather than from field-verified building metadata—can provide a useful learnable context for temporal convolutional network (TCN)-based room-level forecasting when verified semantic metadata are unavailable. Using the public BEAR (Building Efficiency and Renewables) multizone building HVAC dataset, we construct a reproducible operational proxy scenario: eighty rooms are grouped from occupancy, airflow, damper, control-command, and temperature-variability statistics, and Niagara-inspired point-naming conventions are used only as structured metadata descriptors, not as tags exported from a deployed system. The proposed SemanticTCN augments a TCN backbone with learnable room, temperature-variability proxy embeddings. Experiments compare persistence, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), TCN, DLinear, PatchTST, a TCNOnly variant, SemanticTCN, and ablations for 1 h, 6 h, and 24 h forecasts, evaluated by mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination ($R^2$). Persistence is strongest at 1 h and 24 h, while DLinear is strongest at 6 h, indicating strong thermal inertia and daily regularity. Within the TCN family, SemanticTCN improves over TCNOnly in the locked run, shows a statistically significant within-distribution MAE improvement across multiple seeds (most consistently at 6 h), and reduces large-error rates; however, a leave-one-zone-out experiment shows that this advantage does not transfer to rooms unseen during training, and a five-seed operational-versus-random-proxy control shows that the specific operational grouping rule is not statistically distinguishable from arbitrary grouping. The bounded conclusion is that providing room/group context to the TCN backbone helps predict rooms whose identity was observed during training, especially at 6 h, but the gain reflects room-identity context rather than generalizable operational semantics: the proxy labels are heuristic operational constructs rather than verified building semantics; they do not outperform arbitrary grouping under multi-seed control, and they do not generalize to unseen rooms under this split.