A shadow detection and removal method for fruit recognition in natural environments

Abstract

Effective shadow detection and shadow removal can improve the performance of fruit recognition in natural environments and provide technical support for agricultural intelligence. In this study, a superpixel segmentation method was used to divide an image into multiple small regions. Based on the superpixel segmentation results, the shadow regions and the shadowless regions of the orchard images under natural light were compared and studied. Seven shadow saliency features (SSF) were explored and analyzed for shadow detection. The SSF were used to enhance the shadow characteristics. Then, the genetic algorithm (GA) was used to optimize the parameters, and support vector machine recursive feature elimination (SVM-RFE) was used to determine the best feature combination for shadow detection. According to the best feature combination, the support vector machine (SVM) algorithm was used to determine whether each segment of the superpixel segmentation results belonged to the shadow region. Shadow removal was carried out on each detected shadow region, and a natural light image after shadow removal was obtained. Finally, the accuracy of shadow detection was tested. The experimental results showed that the average accuracy of the shadow detection algorithm in this study was 91.91%. As a result, the precision and recall for fruits recognition after shadow removal generally improved.