Organizations: School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China · School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China · Department of computer science, University of North Texas, Denton 76207, Texas, United States · Hangzhou Digitalsalt Technology Co., Ltd., Hangzhou, 310000, China · School of Big Data Intelligent Diagnosis and Treatment Industry, Taiyuan University, Taiyuan 030002, China
Reliable visual monitoring of chemical experiments remains challenging in transparent glassware, where weak phase boundaries and optical artifacts degrade conventional segmentation. We formulate laboratory phenomena as the time evolution of phase interfaces and introduce the Chemical Transparent Glasses dataset 2.0 (CTG 2.0), a vessel-aware benchmark with 3,668 images, 23 glassware categories, and five multiphase interface types for phase-interface instance segmentation. Building on YOLO11m-seg, we propose LGA-RCM-YOLO, which combines Local-Global Attention (LGA) for robust semantic representation and a Rectangular Self-Calibration Module (RCM) for boundary refinement of thin, elongated interfaces. On CTG 2.0, the proposed model achieves 84.4% [email protected] and 58.43% [email protected], improving over the YOLO11m baseline by 6.42 and 8.75 AP points, respectively, while maintaining near real-time inference (13.67 FPS, RTX 3060). An auxiliary color-attribute head further labels liquid instances as colored or colorless with 98.71% precision and 98.32% recall. Finally, we demonstrate continuous process monitoring in separatory-funnel phase separation and crystallization, showing that phase-interface instance segmentation can serve as a practical visual sensor for laboratory automation.
This paper presents an edge-aware instance segmentation framework that enables real-time robotic collision avoidance with transparent laboratory glassware using purely visual perception. Transparent vessels defy conventional segmentation due to refraction, specular reflection, and the absence of stable interior texture, yet their boundary contours remain comparatively reliable visual cues. Exploiting this observation, we augment a one-stage real-time instance segmentation backbone with a lightweight edge-detection branch, edge-guided attention fusion, and a parameter-free SimAM module, and further construct LabGlass-IS, a 3485-image, 21-category instance segmentation dataset of real laboratory glassware. The enhanced model achieves the highest Boundary F-score of 97.80 among compared methods, outperforming the YOLO-prompted FastSAM framework by 18.93 BF points. Furthermore, it maintains an inference speed of 7.1ms per frame and requires only 2.85% of the parameters of the closest accuracy competitor. Multi-view triangulation of mask centroids further provides 3D positions for conservative bounding-volume collision constraints. Real-robot trials achieve a 93.3% collision avoidance success rate, indicating the feasibility of the proposed perception-to-action pipeline for robot collision avoidance among fragile transparent objects. Our code is available at https://github.com/havishamy/TransYOLO_3D. Our video is available at https://havishamy.github.io/paper-videos/.
Liquids like water, wine and medicine are everywhere. However, limited attention has been given to the task of segmenting liquids, hindering the ability of robots to safely avoid and interact with them. The segmentation of liquids is difficult because liquids come in diverse appearances and shapes; moreover, they can be both transparent or reflective, taking on arbitrary objects and scenes from their background and surroundings. To take on this challenge, we construct a liquid dataset, LQDS, consisting of 5000 real-world images annotated into 14 distinct classes, and design a novel liquid detection model, LQDM, which leverages cross-attention between a dedicated boundary branch and the main segmentation branch to enhance mask predictions. Extensive experiments demonstrate the effectiveness of LQDM on the testing set of LQDS, outperforming state-of-the-art methods to establish a strong baseline for the semantic segmentation of liquids. We believe that LQDS and LQDM will facilitate future research in liquid segmentation and enable practical applications in robotics. Our dataset and code is released at https://lonaslee.github.io/LQDM/.
Jonas Li, Michelle Li, Luke Liu +2
Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
Glass surface segmentation from RGB images is a challenging task, with a number of applications in robotics and scene understanding. As glass lacks coherent visual characteristics, rich context and semantic information is crucial for accurate segmentation. Consequently, prior works on the task have explored utilization of foundation models and separate semantic backbones. This paper presents a novel dual-backbone architecture for glass segmentation, applying a frozen foundation model backbone in parallel with a learned backbone trained on task-specific segmentation data. The learned backbone enables the network to specialize to glass related visual clues, while preserving the general visual feature representations of the foundation model. The hierarchical multi-scale features acquired from the dual-backbone are compressed and decoded into segmentation masks. Benchmarking of the architecture was carried out on four commonly used glass segmentation datasets, achieving state-of-the-art results. Ablation studies highlight the performance gains of the dual-backbone design and demonstrate the generalizability of the architecture with different backbone choices. The model also has a competitive inference speed compared to the previous state-of-the-art method, and surpasses it when using a lighter backbone variant. The implementation source code and model weights are available at: https://github.com/ojalar/lgnet.
Risto Ojala, Tristan Ellison, Mo Chen
Aalto University · Espoo, Finland · Simon Fraser University +1