Fabric defect detection plays a pivotal role in achieving intelligent quality control within textile manufacturing. However, intricate background textures, the high visual similarity between defects and the background, and the low proportion of small defects in high-resolution images impede detection accuracy. To address these challenges, we introduce Fabric-DETR, an efficient model for multi-scale fabric defect detection in complex environments. This network integrates three key modules: the Bottle Neck Conv2X Block for enhanced backbone feature extraction, Dynamic Attention-based Intra-scale Feature Interaction to improve attention to small targets, and the Zoom Diffuse Pyramid Network for efficient multi-scale feature fusion. Experiments on a six-class fabric defect dataset demonstrate that Fabric-DETR outperforms existing state-of-the-art methods, achieving a 94.8% mAP, representing improvements of 2.8%, 2.8%, 1.9%, 6.1%, 3.1%, and 2.5% over RT-DETR, YOLOv5-m, YOLOv8-m, YOLOv10-m, YOLOv11-m, and YOLOv12-m, respectively.

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural network with novel attention mechanism modules. We propose an effective channel and spatial Residual Squeeze-and-Excitation (csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiments with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested a few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.