Improved handling of motion blur for grape detection after deblurring

Breakthroughs in the convolution neural network(CNN) have resolved and improved many challenges of pattern recognition in natural images. With the increased use of proximal sensing and low-cost cameras, monitoring and automation systems have gained popularity in the agriculture fields. Detection, segmentation, clustering, and counting are some fundamental problems associated with it. Here we are working on the detection of wine grapes, a crop with a variety of shapes, colors, sizes, and structures. Object detection is a challenging task especially when we are working on natural images. It is an even more difficult task when we are working on blurred images. Blur arises when images are taken via handheld camera, moving object in the automation system, or low rate video frame.Here we are trying to solve the motion blur problem in grape detection using three existing image deblurring algorithms. Performance of the deblurring algorithm is generally measured by peak-signal to noise ratio(PSNR) and structure similarity index(SSIM), but in addition to it, we have also considered blind/referenceless image spatial quality evaluator(BRISQUE). In this paper, we have comparatively analyzed: Scale recurrent network(SRN) for deep image deblurring, Multiscale convolution neural network for dynamic scale deblurring(Deep deblur), and DeblurGANv2: Deblurring(orders of magnitude) faster and better. Grape detection has experimented with yolov5x. Raw images from the standard dataset(GoPro and WGSID) were corrupted with various kinds of motion blurs. From the obtained result we can conclude that image deblurring significantly improves the performance of grape detection on the corrupted motion blur dataset.