New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Abstract

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.

CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection

“…This work is based on a long-term collaboration between the authors' team and industry partners. The achieved results follow the previous publication [9], where the problem of classification and detection of persons in visual data was solved using HOG descriptors; and the publication [10], where an improved DeepLabv3+ semantic segmentation approach was proposed to detect a human head in an RGB image.…”