Detecting The Objects on The Road Using Modular Lightweight Network

TL;DR

This paper introduces a modular lightweight network, MFFD, for efficient small object detection on roads, achieving high accuracy and speed suitable for resource-limited devices like embedded systems.

Contribution

The paper proposes a novel modular architecture with efficient base modules and a multi-scale fusion framework for small object detection, reducing computation and model size.

Findings

Achieves 100 fps on Jetson TX2 embedded GPU.

Outperforms state-of-the-art methods on KITTI dataset.

Effective for resource-constrained environments.

Abstract

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.