ArticleSensors (Basel, Switzerland)2024
Infrared Aircraft Detection Algorithm Based on High-Resolution Feature-Enhanced Semantic Segmentation Network.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features. Secondly, based on the idea of using dilated convolutions to expand the receptive field of feature maps, a hybrid atrous spatial pyramid pooling module is designed. By utilizing a serial structure of dilated convolutions with small dilation rates, this module addresses the issue of feature information loss when expanding the receptive field through dilated spatial pyramid pooling. It captures the contextual information of the target, further enhancing the target features. Finally, a dice loss function is introduced to calculate the overlap between the predicted results and the ground truth labels, facilitating deep excavation of foreground information for comprehensive learning of samples. This paper constructs an infrared aircraft detection algorithm based on a high-resolution feature-enhanced semantic segmentation network which combines the location attention feature fusion network, the hybrid atrous spatial pyramid pooling module, the dice loss function, and a network that maintains the resolution of feature maps. Experiments conducted on a self-built infrared dataset show that the proposed algorithm achieves a mean intersection over union (mIoU) of 92.74%, a mean pixel accuracy (mPA) of 96.34%, and a mean recall (MR) of 96.19%, all of which outperform classic segmentation algorithms such as DeepLabv3+, Segformer, HRNetv2, and DDRNet. This demonstrates that the proposed algorithm can achieve effective detection of infrared aircraft in the presence of interference.
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