Deep Learning for Small and Tiny Object Detection: A Survey

Kos, Aleksandra; Belter, Dominik; Majek, Karol

doi:10.14313/PAR_249/85

Artykuł - szczegóły

Tytuł artykułu

Deep Learning for Small and Tiny Object Detection: A Survey

Autorzy

Kos Aleksandra , Belter Dominik , Majek Karol

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.14313/PAR_249/85

Warianty tytułu

Przegląd metod uczenia głębokiego w wykrywaniu małych i bardzo małych obiektów

Języki publikacji

Abstrakty

In recent years, thanks to the development of Deep Learning methods, there has been significant progress in object detection and other computer vision tasks. While generic object detection is becoming less of an issue for modern algorithms, with the Average Precision for medium and large objects in the COCO dataset approaching 70 and 80 percent, respectively, small object detection still remains an unsolved problem. Limited appearance information, blurring, and low signal-to-noise ratio cause state-of-the-art general detectors to fail when applied to small objects. Traditional feature extractors rely on downsampling, which can cause the smallest objects to disappear, and standard anchor assignment methods have proven to be less effective when used to detect low-pixel instances. In this work, we perform an exhaustive review of the literature related to small and tiny object detection. We aggregate the definitions of small and tiny objects, distinguish between small absolute and small relative sizes, and highlight their challenges. We comprehensively discuss datasets, metrics, and methods dedicated to small and tiny objects, and finally, we make a quantitative comparison on three publicly available datasets.

W ostatnich latach, dzięki rozwojowi metod uczenia głębokiego, dokonano znacznego postępu w detekcji obiektów i innych zadaniach widzenia maszynowego. Mimo że ogólne wykrywanie obiektów staje się coraz mniej problematyczne dla nowoczesnych algorytmów, a średnia precyzja dla średnich i dużych instancji w zbiorze COCO zbliża się odpowiednio do 70 i 80 procent, wykrywanie małych obiektów pozostaje nierozwiązanym problemem. Ograniczone informacje o wyglądzie, rozmycia i niski stosunek sygnału do szumu powodują, że najnowocześniejsze detektory zawodzą, gdy są stosowane do małych obiektów. Tradycyjne ekstraktory cech opierają się na próbkowaniu w dół, które może powodować zanikanie najmniejszych obiektów, a standardowe metody przypisania kotwic są mniej skuteczne w wykrywaniu instancji o małej liczbie pikseli. W niniejszej pracy dokonujemy wyczerpującego przeglądu literatury dotyczącej wykrywania małych i bardzo małych obiektów. Przedstawiamy definicje, rozróżniamy małe wymiary bezwzględne i względne oraz podkreślamy związane z nimi wyzwania. Kompleksowo omawiamy zbiory danych, metryki i metody, a na koniec dokonujemy porównania ilościowego na trzech publicznie dostępnych zbiorach danych.

Słowa kluczowe

Deep Learning Small Object Detection Tiny Object Detection Tiny Object Detection Datasets Tiny Object Detection Methods

uczenie głębokie wykrywanie małych obiektów wykrywanie bardzo małych obiektów zbiory danych bardzo małych obiektów metody wykrywania bardzo małych obiektów

Wydawca

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów PIAP

Czasopismo

Pomiary Automatyka Robotyka

Rocznik

2023

Tom

R. 27, nr 3

Strony

85--94

Opis fizyczny

Bibliogr. 77 poz., tab., wykr.

Twórcy

autor

Kos Aleksandra

aleksandra.kos@doctorate.put.poznan.pl

Poznan University of Technology, Institute of Robotics and Machine Intelligence, 60-965 Poznań, Poland

https://orcid.org/0000-0001-9726-4472

autor

Belter Dominik

dominik.belter@put.poznan.pl

Poznan University of Technology, Institute of Robotics and Machine Intelligence, 60-965 Poznań, Poland

https://orcid.org/0000-0003-3002-9747

autor

Majek Karol

karolmajek@cufix.pl

CuFiX Karol Majek, ul. gen. F. Kleeberga 1A, 05-825 Grodzisk Mazowiecki

https://orcid.org/0000-0002-1351-8496

Bibliografia

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Uwagi

1. The research was supported by the Ministry of Education and Science as part of the ”Doktorat Wdrożeniowy” program (DWD/5/0203/2021).

2. Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-0914b536-d052-40f7-b712-0487146494e0