Enhancing ball detection in football videos using attention mechanisms in FPN-based CNNS
While deep learning models have significantly advanced player detection in sports analytics, accurately identi fying the football remains a persistent challenge due to its small size, rapid movement, frequent occlusions, and visual similarity to other elements such as player socks, logos, and field...
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| Date: | 2025 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
PROBLEMS IN PROGRAMMING
2025
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| Subjects: | |
| Online Access: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/837 |
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| Journal Title: | Problems in programming |
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Problems in programming| Summary: | While deep learning models have significantly advanced player detection in sports analytics, accurately identi fying the football remains a persistent challenge due to its small size, rapid movement, frequent occlusions, and visual similarity to other elements such as player socks, logos, and field markings. This limitation significantly reduces the effectiveness of automated systems in comprehensively analyzing football matches, particularly in applications such as tactical event recognition, shot classification, and game state prediction. In this paper, we propose a method to improve ball detection accuracy in football videos by enhancing an existing architecture based on Feature Pyramid Networks (FPN). The original FPN-based model, although efficient for detecting large-scale players, shows limited performance in detecting small objects such as the ball. To address this, we integrate lightweight attention mechanisms to help the model focus on more relevant spatial and semantic fea tures. Specifically, we introduce Squeeze-and-Excitation (SE) layers into the backbone of the network to perform channel-wise feature recalibration and embed a Convolutional Block Attention Module (CBAM) into the ball detection head to refine both spatial and channel-level attention. These modifications are designed to enhance the network’s ability to distinguish the ball from cluttered backgrounds and visually similar objects. Our exper iments, conducted on the ISSIA-CNR and Soccer Player Detection datasets, demonstrate that the proposed at tention-augmented model achieves improved ball classification accuracy compared to the baseline, with no deg radation in player detection performance. These results validate the utility of lightweight attention mechanisms in the context of small object detection and provide a promising direction for more robust and real-time football video analysis systems.Prombles in programming 2025; 2: 54-62 |
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