In addition, these models also rarely focus on complex objects independently due to the lack of aggregating and exploring the information between the network layers. 36, which utilized transformer-based structure to overcome these issues. Recently, DEtection TRansformer (DETR) was introduced by Carion et al. However, one of the challenges is that neither YOLO nor the SSD can achieve sufficiently high detection accuracy in complex scenes with noisy background images. These models perform well regarding the speed of detection in real-life scenarios. Currently, models such as You Only Look Once (YOLO) and the single-shot detector (SSD) are the most popular DL-based object detectors introduced by Redmon et al. Generally, object detection is described as a collection of related computer vision tasks that involve identifying objects in the given image. However, object detectors for automated material synthesis systems have yet to be reported, and we aim to develop a high-performance detector that is suitable for identifying the error positions of transparent chemical vessels for safety purposes in the present study. ![]() Computer vision has been widely used in object segmentation and detection 24, 25, 26, 27, 28 in industries such as autonomous vehicles 29, 30, disease diagnosis 31, and rehabilitation 32. In this regard, detecting the movements of transparent chemical vessels is considered an important task for improving safety in automated chemical synthesis, and deep learning (DL)-based computer vision can play a vital role here. Some features in the robotic arm such as pressure sensing and control can help reduce the failure cases, however, do not fully eliminate them because undesired external variations can possibly occur unexpectedly. If these vessels are incorrectly placed, any subsequent actions, such as solution stirring, may lead to undesired dangers. To democratize automation in material synthesis laboratories, safety-related issues such as machine control errors need to be addressed.Īutomated material synthesis for bath processes, especially wet chemical-based synthesis, inevitably involves frequent movements of transparent chemical vessels such as flasks 15, 16, 17, 18, beakers 19, 20, 21, and vials 22, 23. Any accidents would cause significant losses of life and property and result in some causalities in severe cases. Although automation can substantially increase developmental efficiency, it is often accompanied by severe dangers in situations where corrosive or inflammable chemicals are handled without human surveillance. Recent works involved the synthesis of a wide range of materials, such as organic/polymer materials 3, 4, 5, 6, 7, 8, 9, quantum dots 10, 11, 12, 13, and nanoparticles 14. Similar content being viewed by othersĪutomated material synthesis based on robotics and artificial intelligence has facilitated the material development process 1, 2. Furthermore, the exceptional detection accuracy of DenseSSD opens up possibilities for its application in various other fields and scenarios where precise object detection is paramount. This study conclusively demonstrates the significant utility of DenseSSD in enhancing safety within automated material synthesis environments. Such robustness of DenseSSD supports that it can universally be implemented in diverse laboratory settings. Additionally, DenseSSD was observed to be generalizable to other laboratory environments, maintaining its high precisions under the variations of solution colors, camera view angles, background scenes, experiment hardware and type of chemical vessels. For the foremost and frequent problem of detecting positions of transparent chemical vessels, DenseSSD achieved a mean average precision (mAP) over 95% based on a complex dataset involving both empty and solution-filled vials, greatly exceeding those of conventional detectors such high precision is critical to minimizing failure-induced accidents. With the aim of improving safety in a surveillance-free laboratory, we report a deep learning (DL)-based object detector, namely, DenseSSD. Object detection techniques can play vital roles in addressing these safety issues however, existing detection models still suffer from insufficient accuracy in environments involving complex and noisy scenes. ![]() Although robot-based automation in chemistry laboratories can accelerate the material development process, surveillance-free environments may lead to dangerous accidents primarily due to machine control errors.
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