The detection and prevention of illegal fishing is critical to maintaining a healthy and functional ecosystem. Recent research on ship detection in satellite imagery has focused exclusively on performance improvements, disregarding detection efficiency. However, the speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing. Therefore, we investigated optimization methods that lower detection time and cost with minimal performance loss. We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images. Then, we designed two efficiency optimizations that can be applied to the base CNN or any other base model. The optimizations consist of a fast, cheap classification model and a statistical algorithm. The integration of the optimizations with the object detection model leads to a trade-off between speed and performance. We studied the trade-off using metrics that give different weight to execution time and performance. We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.

翻译：对非法捕捞的探测和预防对于维持健康和功能生态系统至关重要。最近对卫星图象中船舶探测的研究完全侧重于性能改进,无视探测效率。然而,船只探测的速度和计算成本对于及时采取防止非法捕捞的干预至关重要。因此,我们调查了降低探测时间和成本的优化方法,并尽量减少性能损失。我们利用卫星图像数据集,根据一个卷发神经网络(CNN)培训了一个物体探测模型。然后,我们设计了两个效率优化方法,可以应用于有线电视新闻网的基础模型或任何其他基准模型。优化方法包括快速、廉价的分类模型和统计算法。优化与物体探测模型的整合导致速度和性能的权衡。我们用对执行时间和性能给予不同重量的衡量尺度研究了权衡。我们通过使用一个分类模型,发现模型的平均精确度在44 %的时间里可以接近99.5%,在25%的时间里可以接近92.7%。