Rotten Fruit Detection Using a One Stage Object Detector

Abstract

Digital images and computer sciences have become two powerful tools in several areas, such as astronomy, medicine, forensics, etc. In the last years, computer sciences are getting involved in agricultural and food science to decide based on estimated or actual parameters named features. Rottenness is the state of decomposing or decaying the quality of the fruit, which not only affects the taste and appearance but also modifies its nutritional composition, causing the presence of mycotoxins dangerous for humans. Nowadays, rottenness detection is carried out using human inspection or using Ultraviolet light to highlight spots of rottenness represented as fluorescence. Recent computer vision approaches address this problem using hyperspectral imaging systems. In this paper, we propose to use a one-stage object detector inspired by RetinaNet to detect whether a fruit is fresh or rotten. One of the main stages of RetinaNet is based on computing a multi-scale convolutional feature pyramid network on top of a backbone. Therefore, in this work, we analyze the performance of RetinaNet using different artificial neural networks as backbone to determine the highest accuracy for fruit and rottenness detection. The experiments were done using a dataset composed of 13599 images divided by 6 classes, 3 fresh fruits, and 3 rotten fruits. The performance evaluation considers the mean average precision in the detection and the inference time of tested backbone models.