2026-05-28T14:56:44Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/235292026-02-03T11:17:59Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
García Aguilar, Iván
author
Luque-Baena, Rafael Marcos
author
López-Rubio, Ezequiel
2022-01-07T12:18:19Z
2022-01-07T12:18:19Z
2021
https://hdl.handle.net/10630/23529
10.1111/exsy.12930
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 pretrained 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.
eng
Redes neuronales (Informática)
Improved detection of small objects in road network sequences using CNN and super resolution
journal article
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