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To acquire clear transmittance images of duck eggs, a self-made computer vision system was developed, as shown in Fig 1. This system adopted an Imaging Source color CCD camera (model: DFK23U274) equipped with a manually controlled Pentax TV lens (focal length 16 mm; maximum shutter aperture 1.4). The light source in the egg candler was a LED lamp with a nominal voltage of 12 V, a rated power of 5 W, and a color temperature of 3500 K. To reduce any disturbance to the light from the external environment, the image capture was done in a closed darkroom, a wooden cubic chamber 40×40×50 cm in size and painted black on the inside. The top of the egg candler and the camera lens were set 35 cm apart. During the image capture, the duck egg was set horizontally on the egg candler and an image was taken with an aperture value of 4.0 and exposure of 1/10 s. The resulting images (Fig 2) were saved in BMP format with a resolution of 1600×1200 pixels.
CNNs are feedforward, backpropagate neural networks with a special architecture inspired from the visual system . CNN, as one of the most popular deep learning models at present, has led major advances in the computer vision research community. Compared to conventional machine learning technologies, such as FLD, support vector machine (SVM) and back-propagation neural network (BPNN), CNN allows the net to be fed with raw or only minimally preprocessed images so as to automatically learn the image features needed for recognition and achieves a higher accuracy in practical applications .
25 DY and 25 SY egg images were randomly chosen from the sample images and used as test objects to measure the execution speed (wall clock time) of two whole algorithms based on FLD and CNN models, shown in Fig 9. The codes for the two algorithms were both written using Matlab language. The wall clock time was calculated by employing a stopwatch timer function (based on the tic and toc functions) . All tests were performed on a computer equipped with an Intel Core i7-3537U @ 2.00 GHz processor, 8 GB RAM, Windows 7 system with Matlab R2012b software.
In commercial hatcheries and egg producers, candling is a reliable and unique technique for DY egg identification. The inspection of eggs for double yolks is a major bottleneck because it is largely done by human workers. Candling suffers from judgment errors due mainly to human subjectivity, visual stress, and tiredness, especially when linked with high-speed grading machines. In the last two decades, many researchers have attempted to design and develop computer vision systems to replace human operators for egg quality assessment. In this study, computer vision technology was applied to the identification of DY duck eggs. Compared to candling, computer vision technology can control costs, reduce the workload on workers, and increase the efficiency, accuracy, and stability of the yolk identification process.
This study was conducted in an effort to develop a set of techniques for separating DY from SY duck eggs using computer vision. Both the FLD and CNN model were investigated for duck egg type identification. Training and validation of the FLD model were performed using NFDs extracted from transmittance images of duck egg samples, while those of the CNN model were performed using grayscale images. The classification accuracies of the FLD model for SY and DY eggs were 100% and 93.2% respectively, while the classification accuracies of the CNN model for SY and DY eggs were 98% and 98.8% respectively. The CNN-based algorithm took about 0.12 s to recognize one sample image, which was slightly faster than the FLD-based (about 0.20 s). Both methods can be adapted to the real-time detecting process. The CNN-based method has a better prospect of application, although its algorithm has higher requirement to computer hardware than the FLD-based. The results of this study effectually lay a foundation for the further development of industrial, automatic DY egg sorting equipment based on computer vision. 2b1af7f3a8