Synthetic Aperture Radar (SAR) has the characteristics of all-weather and all-time operation, which can achieve uninterrupted detection of targets on the sea surface. Currently, small-sized ship targets in SAR images are difficult to detect in complex backgrounds due to limited pixel information, unclear azimuth information, and weak signals after imaging. This makes it challenging to detect small-scale ship targets in SAR images. In this paper, we proposed an Enhanced Cascade R-CNN algorithm for detecting small-sized ship targets in complex backgrounds of SAR images. To enhance the multi-scale expression ability of the network, we introduce Res2Net with richer multi-scale information and establish a spatial enhancement module to increase the weight of the ship target in the aspect map. Additionally, a bidirectional feature pyramid structure is constructed to fuse the feature maps output at numerous stages, making the semantic information contained in the feature maps more abundant. To improve the accuracy of the target boundary in dense areas, we introduce a generalized focal loss function and improve the output layer prediction network. Experiments conducted on the SAR-Ship-Dataset show that our algorithm achieves precision, recall, F1, and mAP of 92.6%, 92.4%, 92.8%, and 92.5%, respectively. The proposed method exhibits significant advantages over previous advanced methods in Ship Detection of varying scales in dense scenes. The code will be made available on GitHub.

The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities.