A Progressive Weighted Average Weight Optimisation Ensemble Technique for Fruit and Vegetable Classification
Khurram Hameed, Douglas Chai, Alexander Rassau
Abstract
Image classification of fruit and vegetables at supermarket self-checkouts is a complex problem. Significant variations in the size, shape and colour of objects are involved, along with potentially large variations in the environmental conditions, must be accommodated to implement such a robust and effective system. Convolution Neural Networks (CNNs) have shown promising results for object classifications. However, the scarcity of training datasets due to the diversity of varieties and applications of fruit and vegetable classification is a significant limitation to the CNN implementation for this task. To overcome this, we propose the use of transfer learning and ensemble technique. Specifically, a transfer learning based weighted average weight optimisation ensemble technique is applied to the weights of GoogleNet and MobileNet by transfer learning the pre-trained CNNs using a custom dataset. Two hyperparameter optimisation techniques have been applied in a sequential way to identify the effective weights progressively. The optimised weights are used as an input to a normalised exponential softmax layer to estimate the final probability distribution for classification. A comparative evaluation among standalone GoogleNet, MobileNet and different levels of ensemble has been presented, which supports the adoption of this technique as a solution in a real-world supermarket environment.