# Feature-Fused SSD: Fast Detection for Small Objects

arXiv: https://arxiv.org/abs/1709.05054 · HTML: https://mlsanity.com/paper/1709.05054/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2017.

## Built on by (most cited first, 14 of 14)

- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles](https://mlsanity.com/paper/2112.11798/) (2021)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [Truck Axle Detection with Convolutional Neural Networks](https://mlsanity.com/paper/2204.01868/) (2022)
- [Small Object Detection Based on Modified FSSD and Model Compression](https://mlsanity.com/paper/2108.10503/) (2021)
- [TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors](https://mlsanity.com/paper/2304.03428/) (2023)
- [Detecting Small Objects in Thermal Images Using Single-Shot Detector](https://mlsanity.com/paper/2108.11101/) (2021)
- [Instructive Feature Enhancement for Dichotomous Medical Image Segmentation](https://mlsanity.com/paper/2306.03497/) (2023)
- [Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning](https://mlsanity.com/paper/2306.13020/) (2023)
- [Towards Enhanced Analysis of Lung Cancer Lesions in EBUS-TBNA -- A Semi-Supervised Video Object Detection Method](https://mlsanity.com/paper/2404.01929/) (2024)
- [Multi-SIGATnet: A multimodal schizophrenia MRI classification algorithm using sparse interaction mechanisms and graph attention networks](https://mlsanity.com/paper/2408.13830/) (2024)
- [Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors](https://mlsanity.com/paper/2505.00044/) (2025)
- [Are Candidate Models Really Needed for Active Learning?](https://mlsanity.com/paper/2605.14689/) (2026)
- [Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning](https://mlsanity.com/paper/2605.22169/) (2026)

## Abstract (arXiv)

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCALVOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some smallobjects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS. Code is available at https://github.com/wnzhyee/Feature-Fused-SSD. Keywords: small object detection, feature fusion, real-time, single shot multi-box detector