关键词: 医用塑瓶, 图像处理, 深度学习, 目标检测

Abstract: This paper proposes a recognition method based on deep learning for the real-time detection of production defects such as medical plastic bottle bubbles and accumulated materials, designs the visual inspection hardware platform of the industrial site, describes the principle of the accumulation and bubble detection algorithm, and briefly describes the image pre-processing before the algorithm detection. Under the Pytorch framework, the real-time performance of aggregate detection is compared by orthogonal experiment between ResNet series algorithms and MobilenetV2 algorithm, and the detection performance of RetinaNet network on the bubbles is optimized.At the production site, the average detection accuracy of the proposed method is 99.7% and the single detection time is 29.7 ms. The Fβ index of the bubble is 99.5% and the single detection time is 35.5 ms, which meets the requirements of enterprise production.

Key words: medical plastic bottle, image processing, deep learning, target detection