Enhanced YOLOv7 integrated with small target enhancement for rapid detection of objects on water surfaces - Yu, Jie; Zheng, Hao; Xie, Li; Zhang, Lei; Yu, Mei; Han, Jin (Frontiers in Neurorobotics, 2023)
original version .Unmanned Surface Vessel (USV) target detection technology holds a promising range of applications in coastal defense and control, resource management, and environmental protection. However, the images collected by USVs often pose challenges due to their large target scale variation and susceptibility to interference from complex environments. Existing target detection algorithms frequently exhibit issues such as misdetection and omission of small target objects. To address these challenges, we propose the small target enhanced YOLOv7 (STE-YOLO) approach. Firstly, we introduce a specialized detection branch designed to identify Jie Yu et al.in target scales and susceptibility to interference from complex environments. To address these issues, we propose a small target enhanced YOLOv7 (STE-YOLO) approach. Firstly, we introduce a specialized detection branch designed to identify tiny targets. This enhancement aims to improve the multi-scale target detection capabilities and address difficulties in recognizing targets of different sizes. Secondly, we present the lite visual center (LVC) module, which effectively fuses data from different levels to give more attention to small targets. Additionally, we integrate the lite efficient layer aggregation networks (L-ELAN) into the backbone network to reduce redundant computations and enhance computational efficiency. Lastly, we use Wise-IOU to optimize the loss function definition, thereby improving the model robustness by dynamically optimizing gradient contributions from samples of varying quality. We conducted experiments on the WSODD dataset and the FIOW-Img dataset.The results on the comprehensive WSODD dataset demonstrate that STE-YOLO, when compared to YOLOv7, reduces network parameters by 14% while improving AP50 and APs scores by 2.1% and 1.6%, respectively. Furthermore, when compared to five other leading target detection algorithms, STE-YOLO demonstrates superior accuracy and efficiency.