Small Object Detection

36 papers with code • 4 benchmarks • 10 datasets

Small Object Detection is a computer vision task that involves detecting and localizing small objects in images or videos. This task is challenging due to the small size and low resolution of the objects, as well as other factors such as occlusion, background clutter, and variations in lighting conditions.

( Image credit: Feature-Fused SSD )

Most implemented papers

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

osmr/imgclsmob 22 Apr 2019

As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task.

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

obss/sahi 14 Feb 2022

In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection.

Augmentation for small object detection

zzl-pointcloud/Data_Augmentation_Zoo_for_Object_Detection 19 Feb 2019

We evaluate different pasting augmentation strategies, and ultimately, we achieve 9. 7\% relative improvement on the instance segmentation and 7. 1\% on the object detection of small objects, compared to the current state of the art method on

Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

Jakaria08/Filter_Enhance_Detect 20 Mar 2020

Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance.

Real-time gun detection in CCTV: An open problem

jossalgon/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset 1 Dec 2020

This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi-real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training.

Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss

yangxue0827/RotationDetection 28 Jan 2021

Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.

A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

jwwangchn/NWD 26 Oct 2021

Our key observation is that Intersection over Union (IoU) based metrics such as IoU itself and its extensions are very sensitive to the location deviation of the tiny objects, and drastically deteriorate the detection performance when used in anchor-based detectors.

Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again

wcyjerry/basicisos 28 Jan 2023

On the other hand, FFC can gain image-level receptive fields and extract global information while preventing small objects from being overwhelmed. Experiments on several public datasets demonstrate that our method significantly outperforms the state-of-the-art ISOS models, and can provide useful guidelines for designing better ISOS deep models.

Structure-measure: A New Way to Evaluate Foreground Maps

DengPingFan/S-measure ICCV 2017

Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.