Zhu Qiang, Ma Ke, Wang Zhong, Shi Peibei
School of Computer Science and Technology, Hefei Normal University, Hefei, China.
Front Neurorobot. 2023 Jul 3;17:1210470. doi: 10.3389/fnbot.2023.1210470. eCollection 2023.
The issue of low detection rates and high false negative rates in maritime search and rescue operations has been a critical problem in current target detection algorithms. This is mainly due to the complex maritime environment and the small size of most targets. These challenges affect the algorithms' robustness and generalization.
We proposed YOLOv7-CSAW, an improved maritime search and rescue target detection algorithm based on YOLOv7. We used the K-means++ algorithm for the optimal size determination of prior anchor boxes, ensuring an accurate match with actual objects. The C2f module was incorporated for a lightweight model capable of obtaining richer gradient flow information. The model's perception of small target features was increased with the non-parameter simple attention module (SimAM). We further upgraded the feature fusion network to an adaptive feature fusion network (ASFF) to address the lack of high-level semantic features in small targets. Lastly, we implemented the wise intersection over union (WIoU) loss function to tackle large positioning errors and missed detections.
Our algorithm was extensively tested on a maritime search and rescue dataset with YOLOv7 as the baseline model. We observed a significant improvement in the detection performance compared to traditional deep learning algorithms, with a mean average precision (mAP) improvement of 10.73% over the baseline model.
YOLOv7-CSAW significantly enhances the accuracy and robustness of small target detection in complex scenes. This algorithm effectively addresses the common issues experienced in maritime search and rescue operations, specifically improving the detection rates and reducing false negatives, proving to be a superior alternative to current target detection algorithms.
海上搜索救援行动中检测率低和误报率高的问题一直是当前目标检测算法中的关键问题。这主要是由于复杂的海洋环境和大多数目标的尺寸较小。这些挑战影响了算法的鲁棒性和泛化能力。
我们提出了YOLOv7-CSAW，一种基于YOLOv7改进的海上搜索救援目标检测算法。我们使用K-means++算法来确定先验锚框的最优尺寸，确保与实际物体准确匹配。引入C2f模块以构建能够获得更丰富梯度流信息的轻量级模型。通过非参数简单注意力模块（SimAM）增强模型对小目标特征的感知。我们进一步将特征融合网络升级为自适应特征融合网络（ASFF），以解决小目标中高级语义特征不足的问题。最后，我们实现了明智交并比（WIoU）损失函数来解决大定位误差和漏检问题。
我们的算法在以YOLOv7为基线模型的海上搜索救援数据集上进行了广泛测试。与传统深度学习算法相比，我们观察到检测性能有显著提升，平均精度均值（mAP）比基线模型提高了10.73%。
YOLOv7-CSAW显著提高了复杂场景下小目标检测的准确性和鲁棒性。该算法有效解决了海上搜索救援行动中常见的问题，特别是提高了检测率并减少了误报，证明是当前目标检测算法的优越替代方案。
