Detecting The Objects on The Road Using Modular Lightweight Network

11/16/2018
by   Sen Cao, et al.
0

This paper 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, 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: Front module reduce the information loss from raw input images; Tinier module decrease model size and computation cost, while ensuring the detection accuracy. 2) By stacking the base modules, we design a context features fusion framework for multi-scale object detection. 3) The propose 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 (ADAS). Comparisons with the state-of-the-arts on the challenging KITTI dataset reveal the superiority of the proposed method. Especially, 100 fps can be achieved on the embedded GPUs such as Jetson TX2.

READ FULL TEXT

page 3

page 6

page 9

research
09/15/2017

Feature-Fused SSD: Fast Detection for Small Objects

Small objects detection is a challenging task in computer vision due to ...
research
06/26/2017

Detecting Small Signs from Large Images

In the past decade, Convolutional Neural Networks (CNNs) have been demon...
research
07/18/2023

MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection

In this paper, we propose a novel and effective Multi-Level Fusion netwo...
research
10/11/2016

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

We propose a deep neural network fusion architecture for fast and robust...
research
05/26/2020

Modular WSS-based OXCs for Large-Scale Optical Networks

The explosive growth of broadband applications calls for large-scale opt...
research
06/21/2023

Lightweight wood panel defect detection method incorporating attention mechanism and feature fusion network

In recent years, deep learning has made significant progress in wood pan...
research
10/17/2015

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

The integral image, an intermediate image representation, has found exte...

Please sign up or login with your details

Forgot password? Click here to reset