# Tech Report: One-stage Lightweight Object Detectors

arXiv: https://arxiv.org/abs/2210.17151 · HTML: https://mlsanity.com/paper/2210.17151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 17 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Rethinking Bottleneck Structure for Efficient Mobile Network Design](https://mlsanity.com/paper/2007.02269/) (2020)
- [Accelerator-aware Neural Network Design using AutoML](https://mlsanity.com/paper/2003.02838/) (2020)
- [Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets](https://mlsanity.com/paper/2003.13549/) (2020)
- [PP-LCNet: A Lightweight CPU Convolutional Neural Network](https://mlsanity.com/paper/2109.15099/) (2021)
- [PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices](https://mlsanity.com/paper/2111.00902/) (2021)

## Abstract (arXiv)

This work is for designing one-stage lightweight detectors which perform well in terms of mAP and latency. With baseline models each of which targets on GPU and CPU respectively, various operations are applied instead of the main operations in backbone networks of baseline models. In addition to experiments about backbone networks and operations, several feature pyramid network (FPN) architectures are investigated. Benchmarks and proposed detectors are analyzed in terms of the number of parameters, Gflops, GPU latency, CPU latency and mAP, on MS COCO dataset which is a benchmark dataset in object detection. This work propose similar or better network architectures considering the trade-off between accuracy and latency. For example, our proposed GPU-target backbone network outperforms that of YOLOX-tiny which is selected as the benchmark by 1.43x in speed and 0.5 mAP in accuracy on NVIDIA GeForce RTX 2080 Ti GPU.