Deep Learning based Neck Models for Object Detection: A Review and a Benchmarking Study

Artificial intelligence is the science of enabling computers to act without being further programmed. Particularly, computer vision is one of its innovative fields that manages how computers acquire comprehension from videos and images. In the previous decades, computer vision has been involved in many fields such as self-driving cars, efficient information retrieval, effective surveillance, and a better understanding of human behaviour. Based on deep neural networks, object detection is actively growing for pushing the limits of detection accuracy and speed. Object Detection aims to locate each object instance and assign a class to it in an image or a video sequence. Object detectors are usually provided with a backbone network designed for feature extractors, a neck model for feature aggregation, and finally a head for prediction. Neck models, which are the purpose of study in this paper, are neural networks used to make a fusion between high-level features and low-level features and are known by their efficiency in object detection. The aim of this study to present a review of neck models together before making a benchmarking that would help researchers and scientists use it as a guideline for their works.