Papers › An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks
An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks
Ersin Kılıç, Serkan Öztürk
This paper proposes a simple and effective single-shot detector model to detect and count cars in aerial images. The proposed model, called heatmap learner convolutional neural network (HLCNN), is used to predict the heatmap of target car instances. In order to learn the heatmap of the target cars, we have improved CNN architecture by adding three convolutional layers as adaptation layers instead of fully connected layers. The VGG-16 has been used as a backbone convolutional neural network in the proposed model. The proposed method successfully determines the number of cars and precisely detects the center of target cars. Experiments on the two different car datasets (PUCPR+ and CARPK) show the state-of-the-art counting and localizing performance of the proposed method in comparison with existing methods. Also, experiments have been conducted to examine the effect of data augmentation and batch normalization on the success of the proposed method. The code and data will be made available here [https://www.github.com/ekilic/Heatmap-Learner-CNN-for-Object-Counting].
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Counting | CARPK | HLCNN | MAE | 2.12 | #1 of 15 | Archive leaderboard | report |
| Object Counting | CARPK | HLCNN | RMSE | 3.02 | #1 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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