Tiny target recognition in automation is currently a hot research task that usually suffers from typical issues such as complex background, dim target, and slow detection speed. In the current study, a data-driven method is proposed to realize the posture recognition of micro-lens during optical device coupling to achieve accurate clamping of the gripper. First, we establish a pixel-by-pixel labeled optical micro-lens dataset named single-frame micro-lens target (SFMT), which provides data support for the subsequently proposed convolutional neural network. Subsequently, an asymmetric convolutional multi-level attention network (ACMANet) is proposed to realize accurate segmentation detection of micro-lenses by employing an embedded multi-scale asymmetric convolutional module (MACM) and a multi-level interactive attention module (MIAM). MACM achieves not only a reduction in computational complexity but also enhanced robustness for rotated image recognition through multi-scale asymmetric convolutional kernels. Furthermore, MIAM improves the accuracy of image segmentation by connecting the down-sampling and up-sampling stages and realizing the fusion of pixel position details and key channel features. Extensive experimental results based on our self-constructed image acquisition system demonstrate that the values of normalized intersection over union and dice are successively 91.41% and 95.50%, and the processing speed is 3.3 s/100 images, which shows the advance of ACMANet.

中文翻译：

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用于微透镜分割的非对称卷积多级注意力网络

自动化中的微小目标识别是当前的热门研究任务，通常存在背景复杂、目标昏暗、检测速度慢等典型问题。在当前的研究中，提出了一种数据驱动的方法来实现光学器件耦合过程中微透镜的姿态识别，从而实现夹具的精确夹持。首先，我们建立了一个逐像素标记的光学微透镜数据集，命名为单帧微透镜目标（SFMT），为随后提出的卷积神经网络提供数据支持。随后，提出了一种非对称卷积多级注意网络（ACMANet），通过采用嵌入式多尺度非对称卷积模块（MACM）和多级交互式注意模块（MIAM）来实现微镜头的精确分割检测。 MACM不仅降低了计算复杂度，还通过多尺度非对称卷积核增强了旋转图像识别的鲁棒性。此外，MIAM通过连接下采样和上采样阶段并实现像素位置细节和关键通道特征的融合，提高了图像分割的准确性。基于我们自行构建的图像采集系统的大量实验结果表明，union和dice的归一化交集值分别为91.41%和95.50%，处理速度为3.3 s/100图像，体现了ACMANet的先进性。