WRA-Net:Wide Receptive Field Attention Network for Motion Deblurring in Crop and Weed Image

Automatically segmenting crops and weeds in the image input from cameras accurately is essential in various agricultural technology fields,such as herbicide spraying by farming robots based on crop and weed segmentation information.However,crop and weed images taken with a camera have motion blur due to various causes(e.g.,vibration or shaking of a camera on farming robots,shaking of crops and weeds),which reduces the accuracy of crop and weed segmentation.Therefore,robust crop and weed segmentation for motion-blurred images is essential.However,previous crop and weed segmentation studies were performed without considering motion-blurred images.To solve this problem,this study proposed a new motion-blur image restoration method based on a wide receptive field attention network(WRA-Net),based on which we investigated improving crop and weed segmentation accuracy in motion-blurred images.WRA-Net comprises a main block called a lite wide receptive field attention residual block,which comprises modified depthwise separable convolutional blocks,an attention gate,and a learnable skip connection.We conducted experiments using the proposed method with 3 open databases:BoniRob,crop/weed field image,and rice seedling and weed datasets.According to the results,the crop and weed segmentation accuracy based on mean intersection over union was 0.7444,0.7741,and 0.7149,respectively,demonstrating that this method outperformed the state-of-the-art methods.

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.