Abstract
Localization of fruit and vegetable is of great significance to fruit and vegetable harvesting robots and even harvesting industries. However, uncontrollable factors, such as varying illumination, random occlusion, and various surface color and texture, constrain the localization of fruit and vegetable using the vision imaging technology under unconstructed environment. Our previous studies have developed various methods (illumination normalization, features-based classification, etc.) to localize a certain kind of fruit or vegetable using the binocular stereo vision. However, the localization of the multiple fruit and vegetable still faces challenges due to the uncontrollable factors. In order to address this issue, this study proposed an intelligent localization method of targets in fruit and vegetable images acquired by the two charge-coupled device (CCD) color cameras under unstructured environment. The method utilized the Faster region-based convolutional neural network (R-CNN) model to recognize the fruit and vegetable. Based on the recognition results, a window zooming method was proposed for the matching of the recognized target. Finally, the localization of the target was completed by calculating the three-dimensional coordinates of the matched target using the triangular measurement principle. The experimental results showed that the proposed method could be robust against the influences of varying illumination and occlusion, and the average accurate recognition rate was 96.33% under six different conditions. About 93.44% of 1036 pairs of tested targets from unoccluded and partially occluded conditions were successfully matched. Localization errors had no significant difference and they were less than 7.5 mm when the measuring distance was between 300 and 1600 mm under varying illumination and partially occluded conditions.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
The method was robust to varying illumination and occlusion.
Verbatim
e0“The experimental results showed that the proposed method could be robust against the influences of varying illumination and occlusion, and the average accurate recognition rate was 96.33% under six different conditions.”
t0“Fruit and Vegetable”
Claim · model-written summary
Average accurate recognition rate was 96.33% under six conditions.
Verbatim
e0“the average accurate recognition rate was 96.33% under six different conditions.”
t0“Fruit and Vegetable”
Claim · model-written summary
The method matched 93.44% of 1036 target pairs under unoccluded and partly occluded conditions.
Verbatim
e0“About 93.44% of 1036 pairs of tested targets from unoccluded and partially occluded conditions were successfully matched.”
t0“Fruit and Vegetable”
Claim · model-written summary
Localization errors were below 7.5 mm across 300 to 1600 mm measuring distance.
Verbatim
e0“Localization errors had no significant difference and they were less than 7.5 mm when the measuring distance was between 300 and 1600 mm under varying illumination and partially occluded conditions.”
t0“Fruit and Vegetable”