THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection

Abstract

In boat navigation, especially in complex sea conditions, the detection performance of the tiny boat is related to the safety of boat sailing. However, due to the tiny boat occupying fewer pixels, the effective features of the tiny boat are difficult to obtain. Current tiny object detection methods focus primarily on dataset size matching, feature fusion, and label assignment, lack of attention to texture and detail information loss, and insufficient semantic utilization. Here, we propose a Triple-hierarchy Feature Enhancement (THFE) method to detect tiny boats. The core idea behind THFE is to enhance the semantic information from different layers to supplement the effective features of tiny boats. It consists of three spaces: the super-resolution enhancement space, the semantic enhancement space, and the hierarchical enhancement space. In THFE, sub-pixel convolution, sparse self-attention mechanism, channel attention mechanism, and spatial attention mechanism are adopted to hierarchically enhance each layer’s high-level and low-level semantic features. Finally, each layer’s high-level and low-level semantic features are adaptively fused so that each feature map contains richer high-level and low-level semantic information. Experiments show that our proposed THFE method achieves impressive gains in detection performance. For example, in terms of AP50tiny, our method outperforms state-of-the-art methods by 1.7% on the TinyBoats dataset, 3.1% on the TinyPersons Dataset and 3.9% on the Tiny CityPersons Dataset. To further study the detection of tiny boats, we introduce a tiny boat dataset that will be publicly accessible.