A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits

Abstract

Nondestructive inspection technology based on machine vision can effectively improve the efficiency of fresh fruit quality inspection. However, fruits with smooth skin and less texture are easily affected by specular highlights during the image acquisition, resulting in light spots appearing on the surface of fruits, which severely affects the subsequent quality inspection. Aiming at this issue, we propose a new specular highlight removal algorithm based on multi-band polarization imaging. First of all, we realize real-time image acquisition by designing a new multi-band polarization imager, which can acquire all the spectral and polarization information through single image capture. Then we propose a joint multi-band-polarization characteristic vector constraint to realize the detection of specular highlight, and next we put forward a Max-Min multi-band-polarization differencing scheme combined with an ergodic least-squares separation for specular highlight removal, and finally, the chromaticity consistency regularization is used to compensate the missing details. Experimental results demonstrate that the proposed algorithm can effectively and stably remove the specular highlight and provide more accurate information for subsequent fruit quality inspection. Besides, the comparison of algorithm speed further shows that our proposed algorithm has a good tradeoff between accuracy and complexity.

Nighttime Harvesting of OrBot (Orchard roBot)

“…This was caused by the bright reflection of the light that is present in the center of the apple, which was due to the higher intensity. This specular reflection common for spherical shaped fruits caused the segmentation process to not identify the pixels in this area as part of an apple, which resulted in a hollowed apple shape [22]. This occurrence was also observed during the daytime when the sun's direction is directly hitting a spherical object.…”

Nighttime Harvesting of OrBot (Orchard RoBot)

“…This was caused by the bright reflection of the light that is present in the center of the apple, which was due to the higher intensity. This specular reflection common for spherical-shaped fruits caused the segmentation process to not identify the pixels in this area as part of an apple, which resulted in a hollowed apple shape [22]. This occurrence was also observed during the daytime when the sun's rays directly hit a spherical object.…”

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

“…Many attempts have been made for fruit recognition and classification in robot harvesting and farming using the deep learning approach [ 16 , 17 , 18 ]. A previous study [ 19 ] proposed an improved MobileNetv2 with ImageNet weights and fine-tuning by freezing the first 130 layers of MobileNetV2 and training the remaining 25 layers for fruit classification.…”

“…However, in the majority of the studies [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ], the dataset consisted of a single fruit species under identical illumination conditions, rendering the conclusions less convincing. A further drawback of the existing datasets is that the vast majority of them contain only a small number of fruit types and no vegetable varieties.…”