The ability to recognize a grocery on the shelf of a retail store is an ordinary human skill. Automatic detection of grocery on the shelf of retail store provides enhanced value-added to consumer experience, commercial benefits to retailers and efficient monitoring to domestic enforcement ministry. Compared to machine visionbased object recognition system, automatic detection of retail grocery in a store setting has lesser number of successful attempts. In this paper, we present an enhanced YOLOv4 for grocery detection and recognition. We enhanced through spatial pyramid pooling (SPP) and Intersection over union (IOU) components of YOLOv4 to be more accurate in making recognition and faster in the process. We carried an experiment using modified YOLOv4 algorithm to work with our new customized annotated dataset consist on 12000 images with 13 classes. The experiment result shows satisfactory detection compare to other similar works with mAP of 79.39, IoU threshold of 50%, accuracy of 82.83% and real time performance of 61 frames per second