Lightweight and computationally faster Hypermetropic Convolutional Neural Network for small size object detection

Object detection has been an active area of research over the past two decades. The complexity of detecting an object increases with the increase in object speed and decrease in object size. Similar scenarios are observed in sports video analysis, vision systems of robots, driverless cars and much more. This led to the need for an efficient neural network that can detect small size objects. Further, most of the real-time applications use single board computers such as Jetson Nano, TX2, Xavier, Raspberry Pi and the like. The state-of-the-art of Deep Learning models such as YOLOv4, v3, YOLOR, YOLOX and SSD show poor run-time performance on these devices. Their lighter versions YOLOv3-tiny, YOLOv4-tiny and YOLOX-nano run nearly at 24 frames per second (fps) on Jetson Nano; however, their detection accuracy on small-sized objects is unsatisfactory. This paper focuses on developing a computationally lighter Convolutional Neural network(CNN) to detect small-sized objects efficiently. A novel hypermetropic CNN was developed to meet the above requirements. The improvement in detection is made by extracting more features from the shallow layers and transferring low-level features to the deeper layers. The network is hypermetropic because it performs well on distant objects and lags on nearby objects. The proposed model's performance is compared with the state-of-the-art models on various public datasets such as the VEDAI dataset, Visdrone dataset, and a few classes from the MS COCO and OID dataset. The proposed model shows impressive improvements in detecting small-size objects, and a 32% increase in the fps is observed on Jetson Nano. • A novel CNN architecture to detect small-sized objects is proposed. • Validation is carried out on various public datasets. • Results show impressive improvements in detection accuracy and real-time performance. • It is lighter, smaller and has reduced training time than the state-of-the-art models. • It is suitable for use in any single-board computer and platforms devoid of GPUs.