# DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

arXiv: https://arxiv.org/abs/1903.08589 · HTML: https://mlsanity.com/paper/1903.08589/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [P2T: Pyramid Pooling Transformer for Scene Understanding](https://mlsanity.com/paper/2106.12011/) (2021)
- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
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- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [HR-Crime: Human-Related Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2108.00246/) (2021)
- [Traffic-Net: 3D Traffic Monitoring Using a Single Camera](https://mlsanity.com/paper/2109.09165/) (2021)
- [Crime scene classification from skeletal trajectory analysis in surveillance settings](https://mlsanity.com/paper/2207.01687/) (2022)
- [LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2209.07709/) (2022)
- [Enabling Level-4 Autonomous Driving on a Single 1k Off-the-Shelf Card](https://mlsanity.com/paper/2110.06373/) (2021)
- [CNN-transformer mixed model for object detection](https://mlsanity.com/paper/2212.06714/) (2022)
- [Group channel pruning and spatial attention distilling for object detection](https://mlsanity.com/paper/2306.01526/) (2023)
- [Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons](https://mlsanity.com/paper/2202.07464/) (2022)
- [PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenes](https://mlsanity.com/paper/2203.16301/) (2022)
- [Using Images from a Video Game to Improve the Detection of Truck Axles](https://mlsanity.com/paper/2509.25644/) (2025)

## Abstract (arXiv)

Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is restricted due to the low performance of its backbone network and the underutilization of multi-scale region features. Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for ameliorating the object detection accuracy of YOLOv2 is proposed in this paper. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of MSE (mean square error) loss and cross-entropy loss. The experimental results indicated that the mAP (mean Average Precision) of DC-SPP-YOLO is higher than that of YOLOv2 on the PASCAL VOC datasets and the UA-DETRAC datasets. The effectiveness of DC-SPP-YOLO method proposed is demonstrated.