ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

In addition to reflecting its own health status, fish behavioral changes are also important in analyzing water quality. The accurate and rapid fish detection is the basis for behavioral change analysis. In order to solve the problem of low precision in the existing multitarget fish detection algorithms, a simple but effective multitarget fish detection algorithm was proposed. A new window scoring strategy was created to generate proposal windows by using the skeleton and edge cues of the fish image. The principal component analysis convolution kernels were trained to extract foreground and background features of fish images. The support vector machine was used to classify proposal windows to obtain windows where fish were located, and the nonmaximum suppression algorithm was used to eliminate redundant windows to complete the object detection. Experiments showed that the proposed algorithm based on the new window scoring strategy had a higher recall rate than the Edge Boxes algorithm, and the recall rate was up to 96.9% under the fixed proposals. The highest classification accuracy of proposal windows can reach 95.71%. By analyzing the missed detection rate, false detection rate and average detection time of the algorithm and Edge Boxes-PCANet, the overall performance of the algorithm was optimal. Using this detection algorithm, the multitarget fish detection can be achieved efficiently and accurately.