# HIC-YOLOv5: Improved YOLOv5 For Small Object Detection

arXiv: https://arxiv.org/abs/2309.16393 · HTML: https://mlsanity.com/paper/2309.16393/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [Involution: Inverting the Inherence of Convolution for Visual Recognition](https://mlsanity.com/paper/2103.06255/) (2021)
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://mlsanity.com/paper/2005.03572/) (2020)
- [TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios](https://mlsanity.com/paper/2108.11539/) (2021)

## Built on by (most cited first, 10 of 10)

- [UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery](https://mlsanity.com/paper/2501.01855/) (2025)
- [SO-DETR: Leveraging Dual-Domain Features and Knowledge Distillation for Small Object Detection](https://mlsanity.com/paper/2504.11470/) (2025)
- [An Efficient Aerial Image Detection with Variable Receptive Fields](https://mlsanity.com/paper/2504.15165/) (2025)
- [YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection](https://mlsanity.com/paper/2506.21135/) (2025)
- [High-Frequency Semantics and Geometric Priors for End-to-End Detection Transformers in Challenging UAV Imagery](https://mlsanity.com/paper/2507.00825/) (2025)
- [Maritime Small Object Detection from UAVs using Deep Learning with Altitude-Aware Dynamic Tiling](https://mlsanity.com/paper/2511.19728/) (2025)
- [Better YOLO with Attention-Augmented Network and Enhanced Generalization Performance for Safety Helmet Detection](https://mlsanity.com/paper/2405.02591/) (2024)
- [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)
- [HierLight-YOLO: A Hierarchical and Lightweight Object Detection Network for UAV Photography](https://mlsanity.com/paper/2509.22365/) (2025)
- [YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection](https://mlsanity.com/paper/2606.12958/) (2026)

## Abstract (arXiv)

Small object detection has been a challenging problem in the field of object detection. There has been some works that proposes improvements for this task, such as adding several attention blocks or changing the whole structure of feature fusion networks. However, the computation cost of these models is large, which makes deploying a real-time object detection system unfeasible, while leaving room for improvement. To this end, an improved YOLOv5 model: HIC-YOLOv5 is proposed to address the aforementioned problems. Firstly, an additional prediction head specific to small objects is added to provide a higher-resolution feature map for better prediction. Secondly, an involution block is adopted between the backbone and neck to increase channel information of the feature map. Moreover, an attention mechanism named CBAM is applied at the end of the backbone, thus not only decreasing the computation cost compared with previous works but also emphasizing the important information in both channel and spatial domain. Our result shows that HIC-YOLOv5 has improved mAP@[.5:.95] by 6.42% and mAP@0.5 by 9.38% on VisDrone-2019-DET dataset.