Modular Lightweight Network for Road Object Detection Using a Feature Fusion Approach
| Autor: | Sheng Mei Shen, Sen Cao, Yazhou Liu, Pongsak Lasang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Network architecture business.industry Computer science Deep learning Feature extraction Context (language use) 02 engineering and technology Modular design Object detection Computer Science Applications Human-Computer Interaction 020901 industrial engineering & automation Computer engineering Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Image resolution Software Network model |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51:4716-4728 |
| ISSN: | 2168-2232 2168-2216 |
| DOI: | 10.1109/tsmc.2019.2945053 |
| Popis: | This article presents a modular lightweight network model for road objects detection, such as car, pedestrian, and cyclist, especially when they are far away from the camera and their sizes are small. Great advances have been made for the deep networks, but small objects detection is still a challenging task. In order to solve this problem, a majority of existing methods utilize complicated network or bigger image size, which generally leads to higher computation cost. The proposed network model is referred to as modular feature fusion detector (MFFD), using a fast and efficient network architecture for detecting small objects. The contribution lies in the following aspects: 1) two base modules have been designed for efficient computation: a) Front module reduces the information loss from raw input images and b) Tinier module decreases the model size and computation cost, while ensuring the detection accuracy; 2) by stacking the base modules, we design a context features fusion framework for multiscale object detection; and 3) the proposed method is efficient in terms of model size and computation cost, which is applicable for resource-limited devices, such as embedded systems for advanced driver-assistance systems (ADASs). Comparisons with the state-of-the-art on the challenging KITTI dataset reveal the superiority of the proposed method. Especially, 100 ft/s can be achieved on the embedded GPUs such as Jetson TX2. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|