Kishor Morol

BanglaCrack-10 is a real-world building crack and surface-condition image dataset collected in Bangladesh for computer-vision and automated structural-surface inspection research. The public YOLOv8 release contains 3,603 JPG images from selected sites in Bogura and Dhaka and 3,622 axis-aligned bounding-box annotations across ten classes: Diagonal Cracks, Hairline Cracks, Horizontal Cracks, Map Pattern Cracks, No Crack, Shrinkage Cracks, Spalling Cracks, Stair Step Cracks, Structural Overload Cracks, and Vertical Cracks. Source photographs were acquired using iQOO Neo 11 and vivo Y51 smartphones under natural environmental conditions and varying viewpoints and camera-to-surface distances. The released images are standardized to 640 × 640 pixels and are organized into 2,571 training, 691 validation, and 341 test images. The dataset is intended for reuse in object detection, crack classification, building-surface inspection, transfer learning, and structural-health-monitoring research. Manual screening identified no duplicate images in the released dataset. However, the current partition is not described as building-level or wall-level leakage-free because explicit physical-group identifiers are unavailable and group-level separation has not been independently verified.

Toxic and abusive language on social media is an increasing concern, particularly for low-resource languages such as Bangla, where reliable resources for context-aware and fine-grained toxicity detection remain limited. This data article presents BanglaCAT, a context-aware Bangla social media toxicity dataset that records not only whether a reply is toxic but also the type of toxicity it expresses. Unlike datasets that label comments in isolation, each BanglaCAT record holds a target reply together with the conversational context it appeared under, so that short, ambiguous, sarcastic or context-dependent replies are labeled as they were actually read.The dataset contains 19,453 training context–reply pairs and 2,329 real-world test pairs. The training data were generated with Claude Sonnet 5 and then checked by three human reviewers working independently, each of whom could accept the suggested label or replace it. The final label was decided by majority vote. The test data were collected separately from public Bangla discussions on Facebook, YouTube, and news portals. Each test item was labeled independently by three human annotators. Items that did not receive a 2-of-3 majority on the joint Level-1/Level-2 label were passed to a second, independent group of three annotators, whose own majority decided the label; the two items still unresolved after that round were settled from the pooled votes of both groups.BanglaCAT follows a hierarchical annotation scheme. At Level-1, each target reply is classified as Toxic or Non-toxic. Toxic replies are further assigned one of 11 Level-2 categories: Insult, Threat, Hate Speech, Profanity, Harassment, Identity Attack, Toxic Endorsement / Agreement, Incitement / Encouragement of Harm, Mockery / Ridicule, Sexual Harassment / Objectification, and Dehumanization. The structure supports both binary toxicity detection and finer-grained classification of harmful language in context. To our knowledge, BanglaCAT is the first Bangla resource we have been able to identify that combines paired conversational context, a binary toxicity level, 11 mutually exclusive toxicity subtypes, and a separately collected human-annotated real-world evaluation partition. A Bangla version and a row-aligned English version are released for work on context-aware toxicity detection, hierarchical classification, content moderation, and cross-lingual NLP.