# A Comprehensive Study on Object Detection Techniques in Unconstrained Environments

arXiv: https://arxiv.org/abs/2304.05295 · HTML: https://mlsanity.com/paper/2304.05295/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 2 of 2)

- [Body Part-Based Representation Learning for Occluded Person Re-Identification](https://mlsanity.com/paper/2211.03679/) (2022)
- [Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation](https://mlsanity.com/paper/2112.04840/) (2021)

## Built on by (most cited first, 2 of 2)

- [Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO](https://mlsanity.com/paper/2409.16205/) (2024)
- [Generative data augmentation for biliary tract detection on intraoperative images](https://mlsanity.com/paper/2509.18958/) (2025)

## Abstract (arXiv)

Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches. Additionally, we present a comparative analysis of the methods and highlight their strengths and weaknesses. Finally, we provide some future research directions to further improve object detection in unconstrained environments.