  The aim of this research is to detect small objects with low resolution and
noise. The existing real time object detection algorithm is based on the deep
neural network of convolution need to perform multilevel convolution and
pooling operations on the entire image to extract a deep semantic
characteristic of the image. The detection models perform better for large
objects. The features of existing models do not fully represent the essential
features of small objects after repeated convolution operations. We have
introduced a novel real time detection algorithm which employs upsampling and
skip connection to extract multiscale features at different convolution levels
in a learning task resulting a remarkable performance in detecting small
objects. The detection precision of the model is shown to be higher and faster
than that of the state-of-the-art models.
