LIANG Ziyi, LIU Tianquan, LI Liping, ZHU Yuanfei, LU Cunyue
Accepted: 2025-07-03
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.