Improved detection of small objects in road network sequences using CNN and super resolution

The detection of small objects is one of the problems present in deep learning due to the context of the scene or the low number of pixels of the objects to be detected. According to these problems, current pre-trained models based on convolutional neural networks usually give a poor average precision, highlighting some as CenterNet HourGlass104 with a mean average precision of 25.6%, or SSD-512 with 9%. This work focuses on the detection of small objects. In particular, our proposal aims to vehicle detection from images captured by video surveillance cameras with pre-trained models without modifying their structures, so it does not require retraining the network to improve the detection rate of the elements. For better performance, a technique has been developed which, starting from certain initial regions, detects a higher number of objects and improves their class inference without modifying or retraining the network. The neural network is integrated with processes that are in charge of increasing the resolution of the images to improve the object detection performance. This solution has been tested for a set of traffic images containing elements of different scales to check the efficiency depending on the detections obtained by the model. Our proposal achieves good results in a wide range of situations, obtaining, for example, an average score of 45.1% with the EfficientDet-D4 model for the first video sequence, compared to the 24.3% accuracy initially provided by the pre-trained model.

Automated summary

This research focuses on small object detection in video surveillance images, proposing a method to enhance detection performance without modifying the structure of existing pre-trained models. Experimental results demonstrate the effectiveness of this approach in a variety of traffic images, achieving significant improvement in average scores compared to the initial pre-trained model accuracy.