Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images


Object detection using a boosted cascade of weak classifiers is a principle that has been used in a variety of applications, ranging from pedestrian detection to fruit counting in orchards, and this with a high average precision. In this work we prove that using both the approach suggest by Viola & Jones and the adapted approach by Dollár yields promising results on coconut tree detection in aerial images. However with the rise of robust deep learning algorithms for both detection and classification, and the significant drop in hardware costs, we wonder if it is feasible to apply deep learning to solve the task of fast and robust coconut tree localization in aerial imagery. We examine both pure classification-based and detection-based deep neural networks. By doing so we prove that deep learning is indeed a feasible alternative for coconut tree detection with a high average precision, although caution should be taken and one should keep attention to known issues with the selected architectures.

Proceedings of the 13th international joint conference on computer vision, imaging and computer graphics theory and applications