# Small Object Detection using Context and Attention

arXiv: https://arxiv.org/abs/1912.06319 · HTML: https://mlsanity.com/paper/1912.06319/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [Context Understanding in Computer Vision: A Survey](https://mlsanity.com/paper/2302.05011/) (2023)
- [A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance](https://mlsanity.com/paper/2207.12926/) (2022)
- [Evaluating Context for Deep Object Detectors](https://mlsanity.com/paper/2205.02887/) (2022)
- [Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance](https://mlsanity.com/paper/2111.06830/) (2021)
- [Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion](https://mlsanity.com/paper/2401.08017/) (2024)
- [Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination](https://mlsanity.com/paper/2102.08175/) (2021)
- [SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection](https://mlsanity.com/paper/2201.04235/) (2022)
- [Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions](https://mlsanity.com/paper/2401.10790/) (2024)
- [YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection](https://mlsanity.com/paper/2402.12641/) (2024)
- [GMC: A General Framework of Multi-stage Context Learning and Utilization for Visual Detection Tasks](https://mlsanity.com/paper/2407.05566/) (2024)
- [Chosen methods of improving small object recognition with weak recognizable features](https://mlsanity.com/paper/2208.13591/) (2022)
- [An advanced YOLOv3 method for small object detection](https://mlsanity.com/paper/2212.02809/) (2022)
- [Geometric Perception based Efficient Text Recognition](https://mlsanity.com/paper/2302.03873/) (2023)
- [Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection](https://mlsanity.com/paper/2303.05329/) (2023)
- [CBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer with Block Level CBAM Enhancement](https://mlsanity.com/paper/2409.20113/) (2024)
- [SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object Detection Technology](https://mlsanity.com/paper/2501.12169/) (2025)
- [Multi-Contrast Fusion Module: An attention mechanism integrating multi-contrast features for fetal torso plane classification](https://mlsanity.com/paper/2508.09644/) (2025)
- [Forestpest-YOLO: A High-Performance Detection Framework for Small Forestry Pests](https://mlsanity.com/paper/2510.00547/) (2025)
- [PT-DETR: Small Target Detection Based on Partially-Aware Detail Focus](https://mlsanity.com/paper/2510.26630/) (2025)
- [UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects](https://mlsanity.com/paper/2602.22712/) (2026)
- [Small Object Detection Model with Spatial Laplacian Pyramid Attention and Multi-Scale Features Enhancement in Aerial Images](https://mlsanity.com/paper/2602.23031/) (2026)

## Abstract (arXiv)

There are many limitations applying object detection algorithm on various environments. Especially detecting small objects is still challenging because they have low resolution and limited information. We propose an object detection method using context for improving accuracy of detecting small objects. The proposed method uses additional features from different layers as context by concatenating multi-scale features. We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. Also, for 300×300 input, we achieved 78.1% Mean Average Precision (mAP) on the PASCAL VOC2007 test set.