Automatic seal quality inspection using deep learning in mono material flexible packaging

The automated detection of seal flaws with thermal imaging represents one of the major problems in the manufacturing process. This paper suggests a thermal imaging based deep learning method of detecting sealing faults of mono-material flexible packaging. We compare multiple pre-trained Convolutional Neural Networks to detect more accurately and run faster. Three experimental tests are carried out to enhance the classification process. Another way of reducing model size and computational load with pruning and quantization is required to run it on edge devices. With high-performance NVIDIA GPUs, the adjusted models have an accuracy of 98.7\%, precision of 80.69\%, and recall of 88.89\%. This method performs effectively in real-time seal defect identification and may be applied in packaging quality checks in the industrial line. The model requires tuning to operate more effectively and adapt to a variety of production circumstances and packaging.