UWDET: IoT-Enabled Training Enhancement for Resource-Limited Underwater Object Detection

In Internet of Things (IoT)-enabled marine sensor networks, underwater object detection faces challenges due to resource constraints, such as small object identification and scale variations. These challenges result in sample imbalance and ambiguities in label assignments. While frameworks like the YOLO series are efficient, their underwater performance is often inadequate due to these issues. This paper introduces a training enhancement strategy tailored for underwater object detection (UWDET) in IoT settings, aimed at reducing inference resource consumption while maintaining high detection accuracy. Importantly, our approach preserves existing network architectures and does not extend inference time. The methodology comprises three main elements: Gaussian Overlap Loss (GOL), which interprets bounding boxes through two-dimensional Gaussian distributions, thereby enhancing localization and addressing scale imbalance for small objects in resource-limited environments. Dynamic Task Joint Assignment (DTJA) modifies positive sample assignments based on classification confidence and regression quality, thereby minimizing false positive rates during training. Normative Focal Loss (NFL) employs a normalized joint assignment metric as continuous labels to effectively address sample imbalance. Experimental evaluations on underwater detection benchmarks reveal that our approach markedly enhances precision and recall, stabilizes gradient signals, and improves training efficiency. We also report training-side GPU memory/time/energy and edge-side memory and latency, confirming unchanged inference cost and reduced training resource usage. Our training enhancement strategy applies meticulously designed lightweight generic object detection models to the underwater domain. Without requiring complex modifications to the network architecture, it enables rapid training and facilitates the deployment and inference of high-precision models within resource-constrained IoT underwater devices.