The challenge is formidable. Acetes chinensis individuals measure just three to four centimeters, and in the echograms produced by a scientific echosounder they may occupy only a handful of pixels. The team, led by Yanrui Qu and Sheng Chen of the Third Institute of Oceanography in Xiamen, found that the median shrimp bounding box in their dataset covered just 0.189 percent of the image area. Layer on high-density aggregations, overlapping echoes, reverberation, and background acoustic clutter, and conventional object-detection models struggle badly, missing weak targets or mistaking noise for shrimp.

Acoustic sensing is the right tool for the job because it does not depend on light. Coastal waters near industrial intakes are often turbid, rendering optical cameras nearly useless, while a broadband split-beam echosounder such as the SIMRAD EK80 can operate regardless of visibility. But traditional hydroacoustic analysis, which relies on echo integration and target-strength measurements, estimates aggregate biomass rather than identifying and counting individual animals. Deep-learning detection fills that gap, providing explicit spatial localization that can be combined with tracking for automated enumeration.

The researchers started with RT-DETR, a real-time detection transformer that predicts objects end-to-end without the non-maximum suppression step used by most CNN-based detectors. Its self-attention mechanism models long-range dependencies between target and background regions, which matters when shrimp echoes are embedded in heterogeneous acoustic noise. The baseline RT-DETR-L, initialized with COCO-pretrained weights, already achieved a mean average precision at an intersection-over-union threshold of 0.5, or mAP@50, of 0.818 on their echogram dataset.

Three targeted modifications pushed performance higher. First, the team added a high-resolution P2 detection layer. The original architecture predicts from feature levels P3 through P5, whose spatial resolutions drop as the network deepens, losing fine detail about tiny targets. The new P2 branch fuses upsampled P3 features with shallow backbone features, producing a 160-by-160 feature map for 640-by-640 input, four times the spatial locations of P3, so weak echo boundaries survive the downsampling process.

Second, the researchers integrated Efficient Multi-Scale Attention, or EMA, modules at three backbone stages and three neck stages. These modules adaptively reweight features along both channel and spatial dimensions, amplifying shrimp-related responses while suppressing strong but irrelevant background echoes such as scattering and sidelobe interference. Notably, ablation experiments showed that placement matters: inserting EMA before the Adaptive Intra-scale Feature Interaction module yielded the best results, with mAP@50 rising to 84.2 percent, compared with 82.9 percent when placed between the encoder’s two modules.

Third, the loss function was rebuilt. Because background pixels vastly outnumber shrimp pixels, the team adopted Focal Loss for classification, which down-weights easily classified samples so training focuses on difficult cases. For bounding-box regression they used Wise-IoU version 3, which applies a dynamic non-monotonic focusing mechanism that reduces the influence of low-quality samples on gradient updates. This matters for small boxes, where even a slight displacement of a predicted boundary causes a large drop in overlap with the ground truth. The two losses proved complementary: together they lifted mAP@50-95, a stricter localization metric, to 0.437.

The fully modified model reached a mean mAP@50 of 0.844 and mAP@50-95 of 0.437, with precision of 0.834 and recall of 0.841, all statistically significant improvements over the baseline in paired t-tests. Recall improved by nearly six percentage points, indicating the architecture is particularly good at recovering weak targets that would otherwise slip past. The gains came cheap: parameters rose only from 31.99 million to 32.21 million, and the model outperformed small-object specialists QueryDet and TridentNet while remaining competitive with heavyweight detectors like DINO and RT-DETRv2, which it beat on recall despite their higher mAP scores.

Detection alone, however, cannot count shrimp, because the same individual appears in many consecutive frames. The team therefore coupled their detector with DeepSORT, a tracking algorithm that combines a Kalman filter motion model with appearance embeddings from a Wide Residual Network, matching detections to trajectories via the Hungarian algorithm. Each confirmed trajectory counts as one shrimp. Across ten ten-minute video clips, DeepSORT achieved a counting accuracy of 83.32 percent, outperforming SORT and ByteTrack, which reached 74.88 and 81.08 percent respectively. Qualitative examples showed the improved model dropping a false-positive background response from a confidence of 0.42 to 0.18, below the detection threshold, while boosting a weak shrimp echo from 0.11 to 0.35, rescuing a missed detection.

The authors are candid about limitations. Their data came from a controlled seawater tank, and generalization to different species, depths, densities, and acoustic environments remains untested. Counting errors still arise from trajectory fragmentation, identity switches, and temporary target disappearance, and the added P2 processing slows inference relative to the baseline. Future work will incorporate temporal and multi-frequency acoustic information and more robust trajectory association. Still, the framework demonstrates that deep-learning detection can extend from fish schools to individual planktonic crustaceans, giving nuclear plant operators a practical, non-invasive way to monitor biological loading before a shrimp swarm becomes a shutdown event.

Subject of Research: Deep learning-based detection and counting of Acetes chinensis shrimp in EK80 acoustic echogram images for nuclear power plant intake monitoring

Article Title: Improved RT-DETR-based detection and counting of shrimp in EK80 acoustic echogram images

Article References: Qu, Y., Chen, S., Xue, R., & Zheng, S. (2026). Improved RT-DETR-based detection and counting of shrimp in EK80 acoustic echogram images. Frontiers in Marine Science, 13, Article 1917117. https://doi.org/10.3389/fmars.2026.1917117

Image Credits: AI Generated

DOI: 10.3389/fmars.2026.1917117

Keywords: RT-DETR, deep learning, acoustic echogram, shrimp detection, Acetes chinensis, EK80 echosounder, object detection, multi-object tracking, DeepSORT, nuclear power plant, marine monitoring, small object detection