Effective insect recognition using a stacked autoencoder with maximum correntropy criterion
Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environment is extremely important. Recently, an intelligent trap that uses laser sensors was presented to control the population of target insects. The device could record and analyze sensor signals when an insect passes through the trap and make quick decisions whether to catch it or not. The effectiveness of the trap relies on the correct choice of classification algorithm to perform the insect detection. In this paper, we propose to use a deep neural network with maximum correntropy criterion (MCC) for reliable classification of insects in real-time. Experimental results show that, deep networks are effective for learning stable features from brief insect passage signals. By replacing the mean square error cost with MCC, the robustness of autoencoders against noise is improved significantly and robust features could be learned. The method is tested on five species of insects and a total of 5325 passages. High classification accuracy of 92.1% is achieved. Compared with previously applied methods, better classification performance is obtained using only 10% of the computation time. Therefore, our method is efficient and reliable for online insect detection.