DEVELOPING OBJECT DETECTION ALGORITHM FOR OPTOELECTRONIC SYSTEMS ON SURFACE VESSELS USING DEEP LEARNING MODELS

Abstract

Automatic detection of surface vessels is an important task in maritime surveillance
and security. This paper proposes an improvement to the Ultralytics YOLOv8 model to
achieve higher accuracy and faster processing speed when recognizing surface vessels under
harsh lighting and weather conditions. The paper intergrates three main techniques: a new
C3Plus block, a Position-wise Spatial Attention (PSA) mechanism, and a Convolutional
Block Attention Module (CBAM) module to enhance the network’s feature learning ability.
Experiments on a diverse ship image dataset show that the improved model provides an
increase in mAP of about 3–6% compared to the original YOLOv8 while maintaining a
similar processing speed. In particular, in dark or noisy conditions, the CBAM and PSA
improvements help reduce missing objects and improve the model’s robustness.