Abstract: Detection and identification of aquatic algae is an important task in ecological protection. However, in practical applications, traditional object detection models struggle to meet the real-time and efficiency requirements due to the limited hardware resources of on-site water quality detection equipment, as well as the computational complexity and resource demands. At the same time, lightweight models often face challenges in achieving sufficient accuracy when dealing with issues such as imbalanced sample distribution, severe target occlusion, significant scale differences, and complex backgrounds. To address these challenges, this paper proposes an improved EfficientDet object detection model aimed at effectively improving algae detection performance under limited computational resources. To tackle the problem of insufficient rare algae samples, data augmentation techniques are employed to enhance the model's generalization ability. For algae species with similar features, a CBAM (Convolutional Block Attention Module) attention module is introduced into the backbone network to enhance feature mapping between different algae species. In the feature fusion stage, a BiFPN (Bidirectional Feature Pyramid Network) module based on a hybrid attention mechanism is used to more accurately capture the semantic information of algae in complex backgrounds. Experimental results show that the improved EfficientDet model achieves an average precision (mAP) of 74.2% on the test set, which is a 3.4 percentage point improvement over the original EfficientDet model, with a floating point computation of 21.188 GFLOPS, an energy consumption of only 4.3W, and a model size of 31.4MB, which is just a 0.1MB increase compared to the original model. Compared to YOLOv5s, RetinaNet, Faster R-CNN, SSD, and other mainstream lightweight models such as YOLOv8 and YOLO-WORLD, the average precision (mAP) improved by 7.6, 1.7, 0.7, 4.0, 1.9, and 2.5 percentage points, respectively. Ablation experiments further validate the contribution of each module to the performance improvement and their collaborative optimization effects, providing an efficient and lightweight solution for applications such as water quality monitoring and ecological protection.

摘要： 水体藻类的检测与识别是生态保护中的重要任务。然而，实际应用中，由于现场水质检测设备的硬件资源有限，传统目标检测模型在计算复杂度和算力需求方面难以满足实时性和高效性的要求。同时，轻量级模型在处理样本分布不均、目标遮挡严重、尺度差异显著及背景复杂等问题时，往往面临精度不足的挑战。为此，本文提出了一种改进的EfficientDet目标检测模型，旨在有限计算资源下有效提升藻类检测的性能。针对稀有藻类样本不足的问题，本文采用数据增强技术以增强模型的泛化能力。针对部分藻种特征相似的问题，在骨干网络中引入了CBAM（Convolutional Block Attention Module）注意力模块，增强不同藻种之间的特征映射。在特征融合阶段，采用了基于混合注意力机制的BiFPN(Bidirectional Feature Pyramid Network, Bi-FPN)模块，更精准地捕捉复杂背景下的藻类语义信息。实验结果表明，改进的EfficientDet模型在测试集下的平均精度均值mAP为74.2%，相比原始EfficientDet提升3.4个百分点，浮点计算量为21.188GFLOPS，能耗仅为4.3W，模型大小为31.4MB，较原模型仅增加0.1MB。对比YOLOv5s、RetinaNet、Faster R-CNN、SSD和主流轻量级模型YOLOv8与YOLO-WORLD，平均精度均值mAP分别提升7.6、1.7、0.7、4.0、1.9和2.5个百分点。消融实验进一步验证了各模块对性能提升的贡献及其协同优化效果，为水质监测和生态保护等应用提供了高效、轻量化的解决方案。