Food tray sealing fault detection using hyperspectral imaging and PCANet

Food trays are very common in shops and supermarkets. Fresh food packaged in trays must be correctly sealed to protect the internal atmosphere and avoid contamination or deterioration. Due to the speed of production, it is not possible to have human quality inspection. Thus, automatic fault detection is a must to reach high production volume. This work describes a deep neural network based on Principal Component Analysis Network (PCANet) for food tray sealing fault detection. The input data come from hyperspectral cameras, showing more characteristics than regular industrial cameras or the human eye as they capture the spectral properties for each pixel. The proposed classification algorithm is divided into three main parts. In the first part, a single image is extracted from the hypercube by using pixel-level fusion method: the cube hyperspectral images are transformed into two-dimensional images to use as the input to the PCANet. Second, a PCANet structure is applied to the fused image. The PCANet has two filter bank layers and one binarization layer (three stages), obtaining a feature vector. Finally, a classification algorithm is used, having the feature vector as input data. The SVM and KNN classifiers were used. The database used in this work is provided by food industry professionals, containing eleven types of contamination in the seal area of the food tray and using metallic opaque cover film. Obtained results show that the design of our framework proposed achieves accuracy of 90% (87% F-measure) and 89% (89% F-measure) for SVM and KNN, respectively. Computation time for classification shows that a food tray speed of 65 trays per second could be reached. As a final result, the influence of the dataset size is analyzed, having PCANet a similar behavior for an extended and a reduced dataset

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques

A correct food tray sealing is required to preserve food properties and safety for consumers. Traditional food packaging inspections are made by human operators to detect seal defects. Recent advances in the field of food inspection have been related to the use of hyperspectral imaging technology and automated vision-based inspection systems. A deep learning-based approach for food tray sealing fault detection using hyperspectral images is described. Several pixel-based image fusion methods are proposed to obtain 2D images from the 3D hyperspectral image datacube, which feeds the deep learning (DL) algorithms. Instead of considering all spectral bands in region of interest around a contaminated or faulty seal area, only relevant bands are selected using data fusion. These techniques greatly improve the computation time while maintaining a high classification ratio, showing that the fused image contains enough information for checking a food tray sealing state (faulty or normal), avoiding feeding a large image datacube to the DL algorithms. Additionally, the proposed DL algorithms do not require any prior handcraft approach, i.e., no manual tuning of the parameters in the algorithms are required since the training process adjusts the algorithm. The experimental results, validated using an industrial dataset for food trays, along with different deep learning methods, demonstrate the effectiveness of the proposed approach. In the studied dataset, an accuracy of 88.7%, 88.3%, 89.3%, and 90.1% was achieved for Deep Belief Network (DBN), Extreme Learning Machine (ELM), Stacked Auto Encoder (SAE), and Convolutional Neural Network (CNN), respectively

Evaluation of Dimensionality Reduction Using PCA on EMG-Based Signal Pattern Classification

In this paper, we present a new low-cost system for surface electromyogram (sEMG) acquisition. developed and designed for rehabilitation application purposes. The noninvasive device delivers four-channel EMG bio-signals describing the electrical activity for the right upper limb muscles. The recorded EMG signals obtained from several healthy subjects were exploited to build a database for movement detection and to evaluate the mechanical properties of the upper limb muscles. The proposed study focuses mainly on the influence of the use of the principal component analysis (PCA) method on the movement classification performance based on the sEMG extracted signals. Several tests were conducted, and the simulation results clearly showed the positive impact of PCA as a dimensionality reduction approach with respect to two performance metrics: the classification rate (CR) and the system’s response time. This advantage was confirmed via numerical tests using three different classifiers: K-nearest neighbor (KNN), probabilistic neural network (PNN), and learning vector quantization (LVQ), with and without PCA. The obtained classification rates highlighted the success of the proposed method since a clear improvement in the classification rates was achieved

Behavioural Smoking Identification via Hand-Movement Dynamics

Smoking is a commonly observed habit worldwide, and is a major cause of disease leading to death. Many techniques have been established in medical and psychological science to help people quit smoking. However, the existing systems are complex, and usually expensive. Recently, wearable sensors and mobile application have become an alternative method of improving health. We propose a human behavioural classification based on the use of a one-dimensional local binary pattern (LBP), combined with a Probabilistic Neural Net (PNN) to differentiate smoking from other movements as measured from a wearable device. Human activity signals were recorded from two sets of 6 and 11 participants, using a smart phones equipped with an accelerometer and gyroscope mounted on a wrist module. The data combined structured and naturalistic scenarios. The pro- posed architecture was compared to previously studied machine learning algorithms and found to out-perform them, exhibiting ceiling level performance