Maritime surveillance video (MSV) target detection systems are important for maritime security and ocean economy. Hindered by many complex factors, the existing MSV target detection systems have low detection accuracy. These factors include target distance, potential occlusion from rain and fog, and limited computing power of edge devices. To overcome these factors, a high performance and lightweight maritime target detection algorithm (HPMTD) is proposed in this paper. HPMTD consists of three modules: feature extraction, shallow feature progressive fusion (SFPF), and multi-scale sensing head. In the feature extraction module, a global coordinate attention-optimized offset regression module is proposed for deformable convolution. Thus, the ability to handle low visibility and target occlusion issues is enhanced. In the SFPF module, the ghost dynamic convolution combined with low-cost adaptive spatial feature fusion is proposed. In this way, lightweight design can be realized, and multi-scale target-detecting capacity can be increased. Furthermore, multi-scale sensing head is incorporated to learn and fuse scale features more effectively, thus improving localization accuracy. To evaluate the performance of the proposed algorithm, the Singapore Maritime Dataset is adopted in our experiments. The experimental results show that the proposed algorithm can achieve a nearly 10 percent mean average precision value improvement with nearly half the model size, compared with counterparts. Furthermore, the proposed algorithm runs three times faster with only half of the computation resources, and maintains nearly same accuracy in the maritime surface with low visibility. These results demonstrate that the HPMTD achieves lightweight and high-precision detection of marine targets.

The rapid growth of Internet of Things (IoT) devices has made cyberattacks increasingly covert and evolving. Traditional closed-set intrusion detection systems fail to identify unknown attacks that are unseen during training. Existing open-set methods often rely on a single rejection signal, while multi-representation approaches mainly focus on feature fusion or decision aggregation rather than exploiting complementary uncertainty responses across representations. In addition, probability-based rejection may become less reliable under severe class imbalance. To address these issues, we propose Multi-Representation Confidence Entropy Rejection with Dual-Reject (MRCER-DR), an open-set intrusion detection method for IoT with low inference overhead. It constructs four representations (Raw, Graph, 2D-Stat, and Fusion) and builds a joint rejection score, Confidence Entropy Rejection (CER), combining the maximum class probability and prediction entropy. The Multi-Representation Confidence Ensemble (MRCE) then aggregates the CER scores across representations, while Dual-Reject supplements MRCE with the Mahalanobis distance under severe class imbalance. On UNSW-NB15, MRCER-DR achieves 83.1% unknown recall and an 86.2% AUROC; on ToN-IoT, these reach 97.7% and 90.9%. The single-sample inference latency ranges from 0.04 to 1.01 ms. Without modifying the classifier training, our method balances known-class classification and unknown attack detection, providing a low-overhead approach for IoT open-set intrusion detection.