English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a modular lightweight network model for road objectsdetection, such as car, pedestrian and cyclist, especially when they are faraway from the camera and their sizes are small. Great advances have been madefor the deep networks, but small objects detection is still a challenging task.In order to solve this problem, majority of existing methods utilizecomplicated network or bigger image size, which generally leads to highercomputation cost. The proposed network model is referred to as modular featurefusion detector (MFFD), using a fast and efficient network architecture fordetecting small objects. The contribution lies in the following aspects: 1) Twobase modules have been designed for efficient computation: Front module reducethe information loss from raw input images; Tinier module decrease model sizeand computation cost, while ensuring the detection accuracy. 2) By stacking thebase modules, we design a context features fusion framework for multi-scaleobject detection. 3) The propose method is efficient in terms of model size andcomputation cost, which is applicable for resource limited devices, such asembedded systems for advanced driver assistance systems (ADAS). Comparisonswith the state-of-the-arts on the challenging KITTI dataset reveal thesuperiority of the proposed method. Especially, 100 fps can be achieved on theembedded GPUs such as Jetson TX2.

Detecting The Objects on The Road Using Modular Lightweight Network