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Detecting marine object is an attractive but challenging task in ocean exploration and conservation. Although the current popular object detection algorithms perform well in general. Because underwater images are affected by color projections and the scale of the object is usually smaller, the detection performance for marine object is not ideal. Therefore, this paper proposes an augmented weighted bidirectional feature pyramid network (AWBiFPN) that reduces the weakening of underwater image features and improves the integration efficiency of multi-scale feature, improving marine object detection performance. Specifically, this paper designs a multi-scale feature pyramid for efficient integration of feature information at all levels by combining weighted bidirectional integration pathway with designed Consistent Supervision module. Using Residual Feature Augmentation module to enhance the extraction of unchanging proportional contextual information, reduce the loss of information at the highest level of the feature map in the pyramid network and provide richer particulate characteristics for small-target detection. To scale the feature map and effectively utilize the feature information of different channels in the same spatial location, this article proposes the AWBiFPN layer, which replaces the depthwise separable convolution in the original BiFPN layer with conventional convolution. Evaluated on underwater image dataset (UTDAC), only 12 epochs are trained to 81.63% of mAP; experimented on MS COCO benchmark, achieving 37.9 AP. Meanwhile, the accuracy of the Aquarium Life dataset (AMLD) and underwater detection dataset under natural light (RUOD) are 81.41% mAP and 83.62% mAP, respectively. The results show that the method performed better not only in marine object detection but also in general class detection. • A novel feature pyramid network is proposed for marine object detection. • Three modules are designed to improve detection accuracy and efficiency. • Evaluation tests were conducted on four datasets. • Detection results of various structural designs are displayed. [ABSTRACT FROM AUTHOR]

Traditional object detection methods cannot effectively identify underwater objects with complex backgrounds, and it is difficult to fully obtain the details of small-scale underwater targets, resulting in poor detection performance. We propose a path-augmented Transformer detection framework to address these limitations to explore the semantic details of small-scale underwater targets in complex environments. On one hand, an embedded local path detection information scheme is devised to facilitate the interaction between high-level and low-level features, thereby enhancing the semantic representation of distinctive features of small-scale underwater targets. Rich dependency relationships are established between the acquired high-level and low-level features within the CSWin-Transformer framework, thus fortifying the semantic representation during the encoding phase. Furthermore, a individualized loss function is employed to optimize and fine-tune features at various hierarchical levels. On the other hand, a detection module with flexible and adaptive point representation that is different from conventional square detection methods is designed. This module covers the underwater target from any direction, and the salient point samples in classification localization and feature selection between points realize the feature selection improves the detection accuracy of underwater objects simultaneously. We designed a new weighted loss function to encourage the network to converge better. Experimental results on open-source underwater and remote sensing images of UTDAC, RUOD, and ADios show that the proposed method outperforms other underwater object detection methods in terms of precision(P), recall(R), comprehensive evaluation index of F1-score and FPS(frames per second). • A novel Path Enhanced Transformer is proposed for underwater object detection. • Added semantic representation of salient features of small-scale underwater Objects. • Designed a detection module with flexible, adaptive point representation. • A new weighted loss function is designed. • Evaluation tests were conducted on three datasets. [ABSTRACT FROM AUTHOR]